DIALOGUE SUMMARIZATION

Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining (2021.emnlp-main)

Challenge:	Existing methods for dialogue summarization consider roles separately where interactions among different roles are not fully explored.
Approach:	They propose a novel role-aware centrality model to capture role interactions by involving role prompts to control what kind of summary to generate.
Outcome:	The proposed model achieves state-of-the-art on two public benchmark datasets, CSDS and MC.

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Yicheng Zou, Bolin Zhu, Xingwu Hu, Tao Gui, Qi Zhang

Responsible & Trustworthy NLP

Challenge:	Existing methods for low-resource dialogue summarization neglect the difference between dialogues and conventional articles.
Approach:	They propose a multi-source pretraining paradigm to leverage external summary data . they exploit large-scale in-domain non-summary data to separate dialogue encoder and summary decoder .
Outcome:	The proposed model can be used to better leverage external summary data.

Controllable Neural Dialogue Summarization with Personal Named Entity Planning (2021.emnlp-main)

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Zhengyuan Liu, Nancy Chen

SummerTime: Text Summarization Toolkit for Non-experts (2021.emnlp-demo)

Challenge:	Experimental results show that our proposed framework generates fluent and factually consistent summaries under various planning controls using both objective metrics and human evaluations.
Approach:	They propose a controllable neural generation framework that can guide dialogue summarization with personal named entity planning.
Outcome:	The proposed framework generates fluent and factually consistent summaries under various planning controls using objective metrics and human evaluations.

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Ansong Ni, Zhangir Azerbayev, Mutethia Mutuma, Troy Feng, Yusen Zhang, Tao Yu, Ahmed Hassan Awadallah, Dragomir Radev

SWING: Balancing Coverage and Faithfulness for Dialogue Summarization (2023.findings-eacl)

Challenge:	Recent advances in summarization provide models that can generate high quality summaries . a new toolkit for summarizing text is being developed to make it easier for non-experts to keep track of them.
Approach:	They develop a toolkit for text summarization that integrates with libraries designed for NLP researchers.
Outcome:	SummerTime is a toolkit for text summarization, including models, datasets, and evaluation metrics.

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Kung-Hsiang Huang, Siffi Singh, Xiaofei Ma, Wei Xiao, Feng Nan, Nicholas Dingwall, William Yang Wang, Kathleen McKeown

CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking (2022.aacl-short)

Challenge:	Existing approaches to dialogue summarization rely on features of conversation data.
Approach:	They propose to use natural language inference models to improve coverage and faithfulness . they use fine-grained training signals to encourage model to generate missing content .
Outcome:	The proposed model achieves higher faithfulness and coverage while maintaining conciseness compared to prior methods.

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Haoning Zhang, Junwei Bao, Haipeng Sun, Huaishao Luo, Wenye Li, Shuguang Cui

Low-Resource NLP

Challenge:	Existing few-shot dialogue state tracking (DST) methods transfer knowledge from labeled data into DST, but collecting large amount of labeles is laborious.
Approach:	They propose a few-shot dialogue state tracking framework that integrates self-training and self-supervised learning methods into the framework.
Outcome:	The proposed framework achieves competitive performance in several few-shot scenarios.

DiaSynth: Synthetic Dialogue Generation Framework for Low Resource Dialogue Applications (2025.findings-naacl)

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Sathya Krishnan Suresh, Wu Mengjun, Tushar Pranav, EngSiong Chng

DIONYSUS: A Pre-trained Model for Low-Resource Dialogue Summarization (2023.acl-long)

Challenge:	Existing research is limited by general or niche datasets that lack sufficient scale for training dialogue systems.
Approach:	They propose a synthetic dialogue generation framework that uses Large Language Models and Chain of Thought reasoning to generate dynamic, domain-specific dialogues with simulated personas and diverse conversational features.
Outcome:	The proposed framework outperforms existing frameworks on dialogue summarization and quality increases as the size of the LLM increases from 3B to 8B.

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Yu Li, Baolin Peng, Pengcheng He, Michel Galley, Zhou Yu, Jianfeng Gao

Hierarchical3D Adapters for Long Video-to-text Summarization (2023.findings-eacl)

Challenge:	Existing methods for summarizing dialogues lack in taking into account the structure of dialogues and rely heavily on labeled data.
Approach:	They propose a pre-trained encoder-decoder model for summarizing dialogues in any new domain.
Outcome:	The proposed model outperforms existing methods on six datasets and shows ROUGE scores in zero-shot and few-shot settings.

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Pinelopi Papalampidi, Mirella Lapata

Compact Language Models with Iterative Text Refinement for Health Dialogue Summarization (2026.eacl-long)

Challenge:	a recent study shows that multimodal summarization is not efficient for long inputs and outputs.
Approach:	They extend a TV episode transcript summarization dataset and create a multimodal variant by collecting full-length videos.
Outcome:	The proposed model can be tuned to perform multimodal summarization tasks efficiently using adapter modules augmented with a hierarchical structure while tuning only 3.8% of model parameters.

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Kellen Tan Cheng, Ganesh Ramesh, Nafiul Rashid, Geoffrey Jay Tso, Jilong Kuang

Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization (2021.acl-long)

Challenge:	Health wellness agents typically rely on large language models (LLMs) for response generation, where contextual information from prior conversations can be utilized for response grounding and personalization.
Approach:	They propose to use large language models to generate high-quality health dialogue summaries by using iterative feedback.
Outcome:	The proposed method outperforms baseline on open-source and proprietary benchmarks and can run on local compute without a GPU.

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Xiachong Feng, Xiaocheng Feng, Libo Qin, Bing Qin, Ting Liu

Post-Training Dialogue Summarization using Pseudo-Paraphrasing (2022.findings-naacl)

Challenge:	Existing dialogue summarization systems encode text with a number of general semantic features, but these are often not available in open-domain tools.
Approach:	They propose to use DialoGPT to label three types of features on two datasets . they propose to employ pre-trained and non-pre-tried models as dialogue annotators .
Outcome:	The proposed method improves on two dialogue summarization datasets and achieves state-of-the-art performance.

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Qi Jia, Yizhu Liu, Haifeng Tang, Kenny Zhu

Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer in Prompt Tuning (2024.eacl-long)

Challenge:	Existing approaches to dialogue summarization use dialogue-specific features that require additional knowledge to recognize or make the models harder to tune.
Approach:	They propose to post-train pretrained language models to rephrase from dialogue to narratives and fine-tune them as usual.
Outcome:	The proposed approach outperforms existing models by summary quality and implementation costs.

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Kaige Xie, Tong Yu, Haoliang Wang, Junda Wu, Handong Zhao, Ruiyi Zhang, Kanak Mahadik, Ani Nenkova, Mark Riedl

Low-Resource NLP

Challenge:	Existing prompt transfer techniques lack consideration for dialogue-specific information.
Approach:	They propose a method which leverages skeleton generation as extra supervision that functions as a medium connecting the distinct source and target task.
Outcome:	The proposed method significantly outperforms baselines on two dialogue summarization benchmarks.

DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI (2024.findings-eacl)

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Jianguo Zhang, Kun Qian, Zhiwei Liu, Shelby Heinecke, Rui Meng, Ye Liu, Zhou Yu, Huan Wang, Silvio Savarese, Caiming Xiong

Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions (2022.acl-long)

Challenge:	DialogStudio is the largest and most diverse collection of dialogue datasets . existing datasets lack diversity and comprehensiveness, authors say .
Approach:	They introduce DialogStudio: the largest and most diverse collection of dialogue datasets . DialogStuio aggregates more than 80 diverse dialogue dataset .
Outcome:	a new dataset is created to improve the quality and diversity of dialogue datasets . DialogStudio is the largest and most diverse collection of dialogue data .

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Haitao Lin, Junnan Zhu, Lu Xiang, Yu Zhou, Jiajun Zhang, Chengqing Zong

GENDEX: Generative Data Augmentation Strategy Leveraging External Data for Abstractive Dialogue Summarization (2024.findings-acl)

Challenge:	Existing methods for role-oriented dialogue summarization ignore information from other roles, resulting in omitted information.
Approach:	They propose a novel method that uses cross attention and decoder self-attention interactions to acquire other roles' critical information.
Outcome:	The proposed method significantly outperforms baselines on two public role-oriented dialogue summarization datasets.

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Sangwon Park, Hongseok Choi, Dongha Choi, Hyunju Lee

Chain-of-Interactions: Multi-step Iterative ICL Framework for Abstractive Task-Oriented Dialogue Summarization of Conversational AI Interactions (2025.findings-emnlp)

Challenge:	Existing methods to summarize text data are limited by the lack of data.
Approach:	They propose a method that uses external data to generate synthetic dialogues from short texts containing people and their interpersonal interactions.
Outcome:	The proposed method shows robust performance, generalizability, and scalability regardless of complexity of dialogues.

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Jason S Lucas, Ali Al Lawati, Mahjabin Nahar, John Chen, Mahnoosh Mehrabani

Information Extraction & Text Mining

Challenge:	Large Language Models (LLMs) have introduced paradigm-shifting approaches in natural language processing, yet their transformative in-context learning (ICL) capabilities remain underutilized, especially in customer service dialogue summarization.
Approach:	They propose a single-instance, multi-step framework that orchestrates information extraction, self-correction, and evaluation through sequential interactive generation chains.
Outcome:	The proposed framework outperforms existing models and prompts in the customer service dialogue summarization domain.

TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization (2024.naacl-long)

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Liyan Tang, Igor Shalyminov, Amy Wong, Jon Burnsky, Jake Vincent, Yu’an Yang, Siffi Singh, Song Feng, Hwanjun Song, Hang Su, Lijia Sun, Yi Zhang, Saab Mansour, Kathleen McKeown

Narrate Dialogues for Better Summarization (2022.findings-emnlp)

Challenge:	Existing LLMs hallucinate significant amounts of factual errors in the dialogue domain, regardless of the model’s size.
Approach:	They propose to evaluate topic-focused dialogue summarization by using large language models (LLMs) they use human annotations to evaluate factual consistency and explain factually inconsistent sentences.
Outcome:	The proposed evaluation benchmark on topic-focused dialogue summarization shows that existing LLMs hallucinate significant amounts of factual errors regardless of the model’s size.

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Ruochen Xu, Chenguang Zhu, Michael Zeng

FREDSum: A Dialogue Summarization Corpus for French Political Debates (2023.findings-emnlp)

Challenge:	Recent work on dialogue summarization models focuses on generating concise summaries for multi-party dialogues.
Approach:	They propose several ways to convert dialogue into a third-person narrative style . they propose to use narration as a valuable annotation for LLMs .
Outcome:	Empirical results show that the proposed approach achieves higher scores on ROUGE and a factual correctness metric.

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Virgile Rennard, Guokan Shang, Damien Grari, Julie Hunter, Michalis Vazirgiannis

From spoken dialogue to formal summary: An utterance rewriting for dialogue summarization (2022.naacl-main)

Challenge:	Recent advances in deep learning have improved the performance of abstractive summarization systems.
Approach:	They present a dataset of french political debates to enhance resources for multi-lingual dialogue summarization.
Outcome:	The proposed dataset will be made publicly available for use by the research community.

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Yue Fang, Hainan Zhang, Hongshen Chen, Zhuoye Ding, Bo Long, Yanyan Lan, Yanquan Zhou

Factual Dialogue Summarization via Learning from Large Language Models (2025.coling-main)

Challenge:	Existing models focus more on the structure of summary, not on the personal and logical inconsistency problem.
Approach:	They propose a model to solve the problem of personal and logical inconsistency . they use an utterance rewriter to complete the ellipsis content of dialogue content .
Outcome:	The proposed model outperforms baseline models on both SAMSum and DialSum datasets.

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Rongxin Zhu, Jey Han Lau, Jianzhong Qi

Analyzing and Evaluating Faithfulness in Dialogue Summarization (2022.emnlp-main)

Challenge:	Existing models generate fluent and coherent summaries, but inconsistencies can be found in generated summary.
Approach:	They propose to use symbolic knowledge distillation to improve the factual consistency of smaller pretrained models for dialogue summarization.
Outcome:	The proposed model outperforms baseline models in BART, PEGASUS, and Flan-T5 in factual consistency and accuracy.

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Bin Wang, Chen Zhang, Yan Zhang, Yiming Chen, Haizhou Li

Learn to Copy from the Copying History: Correlational Copy Network for Abstractive Summarization (2021.emnlp-main)

Challenge:	Existing studies on faithfulness of text summarization have not been conducted on abstractive summarizing.
Approach:	They propose a method to evaluate faithfulness of dialogue summarization models by multi-choice questions.
Outcome:	The proposed method can facilitate the development of dialogue summarization systems.

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Haoran Li, Song Xu, Peng Yuan, Yujia Wang, Youzheng Wu, Xiaodong He, Bowen Zhou

Exploring the Factual Consistency in Dialogue Comprehension of Large Language Models (2024.naacl-long)

Challenge:	Existing methods for abstractive summarization use encoder-decoder attention, but this leads to incomplete copying.
Approach:	They propose a copying scheme that takes advantage of prior copying distributions and explicitly encourages the model to copy the input word that is relevant to the previously copied one.
Outcome:	The proposed scheme achieves state-of-the-art on summarization benchmarks . it takes advantage of prior copying distributions and explicitly encourages copying .

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Shuaijie She, Shujian Huang, Xingyun Wang, Yanke Zhou, Jiajun Chen

Topic-Informed Dialogue Summarization using Topic Distribution and Prompt-based Modeling (2023.findings-emnlp)

Challenge:	LLMs generate responses following user's instructions, which requires high dialogue comprehension ability.
Approach:	They propose to evaluate LLMs' dialogue comprehension ability using a dialogue summarization task to derive factual questions from the generated summaries and use them as a more flexible measurement of dialogue comprehension.
Outcome:	The proposed model reduces the error rate by 11% on the dialogue summarization task.

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Jaeah You, Youngjoong Ko

Effectiveness of French Language Models on Abstractive Dialogue Summarization Task (2022.lrec-1)

Challenge:	Existing models that deal with multiple topics are prone to topic drift . experimental results show that our model outperforms state-of-the-art methods on ROUGE scores .
Approach:	They propose a dialogue summarization model that reflects dialogue topic distribution to consider all topics present in a dialog.
Outcome:	The proposed model outperforms state-of-the-art methods on ROUGE scores and generates comprehensive summaries.

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Yongxin Zhou, François Portet, Fabien Ringeval

Language Models

Challenge:	Pre-trained language models have established the state-of-the-art on various natural language processing tasks, including dialogue summarization.
Approach:	They propose to use several language specific pre-trained models to summarize spontaneous oral dialogues in French using several language-specific pre-trainers: BARThez, BelGPT-2, mBARThes, and mT5.
Outcome:	The proposed models outperform the existing models on the DECODA (Call Center) dialogue corpus and show that they are far superior to the current models.

Dialogue Summarization with Mixture of Experts based on Large Language Models (2024.acl-long)

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Yuanhe Tian, Fei Xia, Yan Song

Mutual Reinforcement of LLM Dialogue Synthesis and Summarization Capabilities for Few-Shot Dialogue Summarization (2025.findings-naacl)

Challenge:	Existing studies for dialogue summarization use one model at a time or treat it as a black box.
Approach:	They propose an LLM-based approach with role-oriented routing and fusion generation to utilize mixture of experts for dialogue summarization.
Outcome:	The proposed approach produces informative and accurate dialogue summarization on widely used datasets.

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Yen-Ju Lu, Ting-Yao Hu, Hema Swetha Koppula, Hadi Pouransari, Jen-Hao Rick Chang, Yin Xia, Xiang Kong, Qi Zhu, Xiaoming Simon Wang, Oncel Tuzel, Raviteja Vemulapalli

DialogSum: A Real-Life Scenario Dialogue Summarization Dataset (2021.findings-acl)

Challenge:	Empirical results demonstrate that our method improves dialogue summarization, achieving a 1.5% increase in ROUGE scores and a 0.3% improvement in BERT scores in few-shot settings.
Approach:	They propose Mutual Reinforcing Data Synthesis (MRDS) within large language models to enhance few-shot dialogue summarization task.
Outcome:	Empirical results show that the proposed method improves dialogue summarization, achieving a 1.5% increase in ROUGE scores and a 0.3% improvement in BERT scores in few-shot settings.

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Yulong Chen, Yang Liu, Liang Chen, Yue Zhang

Multi-Stage Pre-training Enhanced by ChatGPT for Multi-Scenario Multi-Domain Dialogue Summarization (2023.findings-emnlp)

Challenge:	Experimental results show unique challenges in dialogue summarization such as spoken terms, special discourse structures, coreferences and ellipsis, pragmatics and social common sense.
Approach:	They propose a large-scale labeled dialogue summarization dataset . they use state-of-the-art neural models to analyze spoken dialogue summaries .
Outcome:	The proposed dataset can be used to analyze spoken dialogue summarization challenges.

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Weixiao Zhou, Gengyao Li, Xianfu Cheng, Xinnian Liang, Junnan Zhu, Feifei Zhai, Zhoujun Li

Language Models

Challenge:	Existing methods for dialogue summarization only apply to specific scenarios and domains.
Approach:	They propose a pre-trained model specifically designed for multi-scenario multi-domain dialogue summarization.
Outcome:	The proposed model significantly outperforms state-of-the-art models on three dialogue summarization datasets from different scenarios and domains.

RepSum: Unsupervised Dialogue Summarization based on Replacement Strategy (2021.acl-long)

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Xiyan Fu, Yating Zhang, Tianyi Wang, Xiaozhong Liu, Changlong Sun, Zhenglu Yang

MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization (2021.naacl-main)

Challenge:	Existing methods to learn vital information from dialogue context with limited data are limited due to limited words in utterances and huge gap between dialogue and its summary.
Approach:	They propose an unsupervised strategy to learn vital information from dialogue context . the proposed model uses a hypothetical foundation that a superior summary approximates a replacement of the original dialogue .
Outcome:	The proposed model outperforms existing models on a number of datasets.

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Chenguang Zhu, Yang Liu, Jie Mei, Michael Zeng

Instructive Dialogue Summarization with Query Aggregations (2023.emnlp-main)

Challenge:	Existing datasets for dialogue summarization are limited to their small sizes and are built from a narrow domain.
Approach:	They propose a large-scale media interview dataset consisting of 463.6K transcripts with abstractive summaries.
Outcome:	The proposed dataset is larger and contains multi-party conversations from multiple domains.

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Bin Wang, Zhengyuan Liu, Nancy Chen

View Dialogue in 2D: A Two-stream Model in Time-speaker Perspective for Dialogue Summarization and beyond (2022.coling-1)

Challenge:	Conventional dialogue summarization methods generate summaries without considering user’s specific interests.
Approach:	They propose a three-step approach to synthesize high-quality query-based summarization triples by training a unified model on three summarizing datasets with multi-purpose instructive triples.
Outcome:	The proposed model outperforms state-of-the-art models and even models with larger sizes on four datasets including dialogue summarization and dialogue reading comprehension.

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Keli Xie, Dongchen He, Jiaxin Zhuang, Siyuan Lu, Zhongfeng Wang

Improving Abstractive Dialogue Summarization with Speaker-Aware Supervised Contrastive Learning (2022.coling-1)

Challenge:	Existing models for dialogue summarization focus on document summarizing on time and speaker-centered points, but this approach is limited in understanding the dialogue.
Approach:	They propose a 2D view of dialogue based on a time-speaker perspective where the time and speaker streams of dialogue can be obtained as strengthened input.
Outcome:	The proposed model outperforms existing models on the QMSum dataset and improves summary faithfulness and human evaluation.

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Zhichao Geng, Ming Zhong, Zhangyue Yin, Xipeng Qiu, Xuanjing Huang

ORCHID: A Chinese Debate Corpus for Target-Independent Stance Detection and Argumentative Dialogue Summarization (2023.emnlp-main)

Challenge:	Existing summarization systems based on pre-trained models cannot recognize the unique format of the speaker-utterance pair well in the dialogue.
Approach:	They propose three speaker-aware supervised contrastive learning tasks to solve the speaker identification problem in dialogue summarization task.
Outcome:	The proposed methods improve on two mainstream dialogue summarization datasets.

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Xiutian Zhao, Ke Wang, Wei Peng

Analyzing LLM Behavior in Dialogue Summarization: Unveiling Circumstantial Hallucination Trends (2024.acl-long)

Challenge:	Recent development of large language models (LLMs) have boosted interest on dialogue agents . however, research on these tasks is limited by the insufficiency of public datasets . stance detection and debate summarization are key for engaging argumentative dialogues - but are not available for non-English languages.
Approach:	They propose to use ORCHID to benchmark stance detection and debate summarization in Chinese debates.
Outcome:	The proposed task is based on 1,218 real-world debates conducted in Chinese on 476 unique topics.

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Sanjana Ramprasad, Elisa Ferracane, Zachary Lipton

Summarization

Challenge:	Recent advances in large language models have improved summarization, but they still face a challenge of hallucination.
Approach:	They propose a taxonomy of errors to address the problem of hallucination in LLMs . they propose two prompt-based approaches for fine-grained error detection .
Outcome:	The proposed model outperforms existing metrics in identifying the novel "Contextual Inference" error type.

Reference Matters: Benchmarking Factual Error Correction for Dialogue Summarization with Fine-grained Evaluation Framework (2023.acl-long)

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Mingqi Gao, Xiaojun Wan, Jia Su, Zhefeng Wang, Baoxing Huai

Improving Role-Oriented Dialogue Summarization with Interaction-Aware Contrastive Learning (2024.lrec-main)

Challenge:	Current evaluations of FEC models that depend on factuality metrics are not reliable and detailed enough.
Approach:	They propose a fine-grained evaluation framework that automatically evaluates FEC models on different error categories.
Outcome:	The proposed evaluation framework compares models on different error categories and finds the best training modes and significant differences in the performance of existing models.

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Weihong Guan, Shi Feng, Daling Wang, Faliang Huang, Yifei Zhang, Yuan Cui

Towards Understanding Omission in Dialogue Summarization (2023.acl-long)

Challenge:	Existing methods for encoding dialogues do not capture interaction information between roles, thus ignore interaction-related key information.
Approach:	They propose a contrastive learning based interaction-aware model for the role-oriented dialogue summarization namely CIAM and use it to train the decoder to learn role-level interaction.
Outcome:	The proposed model captures interaction information between different roles and produces informative summaries on two public datasets.

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Yicheng Zou, Kaitao Song, Xu Tan, Zhongkai Fu, Qi Zhang, Dongsheng Li, Tao Gui

Task-Aware Self-Supervised Framework for Dialogue Discourse Parsing (2023.findings-emnlp)

Challenge:	Existing methods for dialogue summarization are far from satisfactory . omission is a major factor in affecting the quality of summarizing, but few studies have explored the problem .
Approach:	They propose a dataset that provides high-quality omission labels for dialogue summarization . they propose to use this dataset to detect omitted dialogue utterances .
Outcome:	The proposed dataset improves summarization quality by providing ground-truth omission labels . the proposed dataset and codes are publicly available .

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Wei Li, Luyao Zhu, Wei Shao, Zonglin Yang, Erik Cambria

MDS: A Fine-Grained Dataset for Multi-Modal Dialogue Summarization (2024.lrec-main)

Challenge:	Existing discourse parsing approaches are constrained by predefined relation types, which can impede the adaptability of the parser for downstream tasks.
Approach:	They propose to introduce a task-aware paradigm to improve the versatility of the parser.
Outcome:	Empirical studies on dialogue discourse parsing datasets and a downstream task demonstrate the proposed framework.

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Zhipeng Liu, Xiaoming Zhang, Litian Zhang, Zelong Yu

Paired by the Teacher: Turning Unpaired Data into High-Fidelity Pairs for Low-Resource Text Generation (2025.emnlp-main)

Challenge:	Summarizing the dialogue into a short message has drawn much attention due to the explosion of various dialogue scenes.
Approach:	They develop a multi-modal dialogue summarization dataset to enhance the variety of data available for this research area.
Outcome:	The proposed dataset provides a demanding testbed for multi-modal dialogue summarization.

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Yen-Ju Lu, Thomas Thebaud, Laureano Moro-Velazquez, Najim Dehak, Jesus Villalba

Beyond Overlap Metrics: Rewarding Reasoning and Preferences for Faithful Multi-Role Dialogue Summarization (2026.findings-acl)

Challenge:	a low-resource natural language generation task requires a large number of examples to generate outputs and outputs.
Approach:	They propose a teacher-student pipeline that synthesizes accurate input–output pairs without human labels or parallel data.
Outcome:	The proposed pipeline synthesizes accurate input–output pairs without human labels or parallel data.

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Xiaoyong Mei, Tingting Zuo, Da Chen, Guangyu Hu, Xiangyu Wen, Chao Duan, Mingyan Zhang, Fudan Zheng