Papers with ROUGE-L
“Knowledge is Power”: Constructing Knowledge Graph of Abdominal Organs and Using Them for Automatic Radiology Report Generation (2023.acl-industry)
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Kaveri Kale, Pushpak Bhattacharyya, Aditya Shetty, Milind Gune, Kush Shrivastava, Rustom Lawyer, Spriha Biswas
| Challenge: | conventional radiology workflows involve dictating diagnosis to transcriptionists, which is prone to delay and error. |
| Approach: | They propose to generate a set of knowledge graphs from a large collection of free-text radiology reports and use them to generate automatic radiology report generation. |
| Outcome: | The proposed model improves the reported BLEU-3, ROUGE-L, METEOR, and CIDEr scores by 2%, 4%, 2% and 2% respectively. |
Copyright Detective: A Forensic System to Evidence LLMs Flickering Copyright Leakage Risks (2026.acl-demo)
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Guangwei Zhang, Jianing Zhu, Cheng Qian, Neil Zhenqiang Gong, Rada Mihalcea, Zhaozhuo Xu, Jingrui He, Jiaqi W. Ma, Chaowei Xiao, Bo Li, Ahmed Abbasi, Dongwon Lee, Heng Ji, Denghui Zhang
| Challenge: | **Copyright Detective** is the first interactive forensic system for detecting, analyzing, and visualizing potential copyright risks in LLM outputs. |
| Approach: | They propose a system that detects copyright infringements and visualizes them . they use content recall testing, paraphrase-level similarity analysis and persuasive jailbreak probing . |
| Outcome: | The proposed system detects, analyzes, and visualizes potential copyright risks in LLM outputs. |
Long Document Summarization in a Low Resource Setting using Pretrained Language Models (2021.acl-srw)
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Ahsaas Bajaj, Pavitra Dangati, Kalpesh Krishna, Pradhiksha Ashok Kumar, Rheeya Uppaal, Bradford Windsor, Eliot Brenner, Dominic Dotterrer, Rajarshi Das, Andrew McCallum
| Challenge: | Existing abstractive summarization methods only achieve 17.9 ROUGE-L in low-resource settings. |
| Approach: | They propose to use a modern abstractive summarization algorithm to extract salient sentences from long documents to improve their performance. |
| Outcome: | The proposed method beats several competitive salience detection baselines and the identified salient sentences agree with independent human labeling by domain experts. |
Towards Making the Most of Cross-Lingual Transfer for Zero-Shot Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing unsupervised neural machine translation systems can degrade when labeled data is limited. |
| Approach: | They propose a multilingual pretraining and multilingual fine-tuning for facilitating cross-lingual transfer in zero-shot translation using a parallel dataset. |
| Outcome: | The proposed model outperforms state-of-the-art models on many-to-English translation by over 7.2 and 5.0 BLEU. |
Hie-BART: Document Summarization with Hierarchical BART (2021.naacl-srw)
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| Challenge: | Existing document summarization models do not capture hierarchical structures of documents . proposed model incorporates multi-granularity self-attention (MG-SA) |
| Approach: | They propose a new abstractive document summarization model, hierarchical BART . the proposed model captures hierarchically structured sentences in the BART model . |
| Outcome: | The proposed model outperforms baseline models and improves performance on CNN/Daily Mail dataset. |
DESCGEN: A Distantly Supervised Datasetfor Generating Entity Descriptions (2021.acl-long)
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| Challenge: | Short textual descriptions of entities provide summaries of their key attributes but generating entity descriptions can be challenging since information is scattered across multiple sources with varied content and style. |
| Approach: | They propose to generate an entity summary description from 37K entities from Wikipedia and Fandom, paired with nine evidence documents on average. |
| Outcome: | The proposed task is entity-centric, more abstractive, and covers a wide range of domains. |
Cut to the Chase: A Context Zoom-in Network for Reading Comprehension (D18-1)
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| Challenge: | Recent deep-learning based models suffer from reasoning over long documents and do not trivially generalize to cases where the answer is not present as a span. |
| Approach: | They propose a novel context zoom-in network (ConZNet) that can skip through irrelevant parts of a document and generate an answer using only the relevant regions of text. |
| Outcome: | The proposed architecture outperforms state-of-the-art results by 12.62% (ROUGE-L) relative improvement on the recently proposed and challenging RC dataset ‘NarrativeQA’. |
Revisiting Evaluation of Question Answering Systems in Low-Resource Indic Languages: Bridging Human and Metric Alignment (2026.acl-short)
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| Challenge: | Evaluating Question Answering systems in low-resource Indic languages remains challenging due to the scarcity of annotated data and the lack of reliable evaluation metrics. |
| Approach: | They propose a language-based multi-aspect evaluation framework for question answering systems . the framework integrates semantic similarity, factual completeness, numerical accuracy and contextual relevance . |
| Outcome: | The proposed metric is evaluated across eight Indic-language QA tasks using multiple LLMs . Across all settings, it shows stronger agreement with human evaluation . |
Revisiting Uncertainty Quantification Evaluation in Language Models: Spurious Interactions with Response Length Bias Results (2025.acl-short)
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Andrea Santilli, Adam Golinski, Michael Kirchhof, Federico Danieli, Arno Blaas, Miao Xiong, Luca Zappella, Sinead Williamson
| Challenge: | Language Models (LMs) produce factually incorrect outputs, or "hallucinations" Xiao and Wang et al., 2023) rely on AUROC to assess how well UQ methods distinguish correct from incorrect output. |
| Approach: | They propose to use length biases in correctness functions to skew UQ evaluations . they propose to employ LM-as-a-judge methods as the least length-biased . |
| Outcome: | The proposed method is least length-biased, offering a promising path for a fairer evaluation. |
ELMER: A Non-Autoregressive Pre-trained Language Model for Efficient and Effective Text Generation (2022.emnlp-main)
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| Challenge: | Existing methods for text generation use auto-regressive (AR) methods, but inefficient inference is a problem. |
| Approach: | They propose an efficient and effective PLM to explicitly model the token dependency during NAR text generation. |
| Outcome: | The proposed model outperforms existing models on three text generation tasks while achieving 10 times faster inference speedup. |
LLMRefine: Pinpointing and Refining Large Language Models via Fine-Grained Actionable Feedback (2024.findings-naacl)
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Wenda Xu, Daniel Deutsch, Mara Finkelstein, Juraj Juraska, Biao Zhang, Zhongtao Liu, William Yang Wang, Lei Li, Markus Freitag
| Challenge: | Recent large language models (LLMs) are leveraging human feedback to improve their output quality. however, human feedback is costly to collect, especially at inference time when the model provides new, unseen input. |
| Approach: | They propose an inference-time optimization method to refine large language models' output based on fine-grained feedback to pinpoint defects and guide iterative refinement . |
| Outcome: | The proposed method consistently outperforms baseline approaches on three text generation tasks, including machine translation, long-form question answering, and topical summarization. |
EXPLAIN: Enhancing Retrieval-Augmented Generation with Entity Summary (2025.acl-industry)
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Yaozhen Liang, Xiao Liu, Jiajun Yu, Zhouhua Fang, Qunsheng Zou, Linghan Zheng, Yong Li, Zhiwei Liu, Haishuai Wang
| Challenge: | Existing document question answering methods reduce inference costs and input tokens. |
| Approach: | They propose a retrieval-augmented generation method that automatically extracts useful entities and generates summaries from documents. |
| Outcome: | The proposed method surpasses baseline retrieval-augmented generation (RAG) and long-context question answering (LC) methods achieve higher accuracy by processing entire documents, but at the cost of increased computational Corresponding authors. |
Exploring Neural Models for Query-Focused Summarization (2022.findings-naacl)
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| Challenge: | Recent work in Query-focused summarization lacks a comprehensive study of the broad space of applicable modeling methods. |
| Approach: | They propose to explore two general classes of methods for Query-focused summarization: extractive-abstractive solutions and end-to-end models. |
| Outcome: | The proposed models achieve state-of-the-art on the QMSum dataset, with a margin of 3.38 ROUGE-1, 3.72 ROUGe2 and 3.28 ROUGEL-L. |
DETQUS: Decomposition-Enhanced Transformers for QUery-focused Summarization (2025.naacl-long)
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Yasir Khan, Xinlei Wu, Sangpil Youm, Justin Ho, Aryaan Mehboob Shaikh, Jairo Garciga, Rohan Sharma, Bonnie J Dorr
| Challenge: | Query-focused tabular summarization is an emerging task in table-to-text generation . traditional transformer-based approaches face challenges due to token limitations and the complexity of reasoning over large tables. |
| Approach: | They propose a system that leverages tabular decomposition alongside a fine-tuned encoder-decoder model to improve summarization accuracy. |
| Outcome: | a new system outperforms the state-of-the-art REFACTOR model in a Query-focused tabular summarization task . the proposed system achieves a ROUGE-L score of 0.4437, outperforming the previous state- of-the art model . |
Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts (2024.acl-long)
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| Challenge: | Large language models (LLMs) are known to perform tasks by simply observing few exemplars, but performance among under-represented languages falls behind due to pre-training data imbalance. |
| Approach: | They propose to assemble synthetic exemplars from high-resource languages to prompt LLMs to translate from any language into English and use them to create intra-lingual exemplar models to perform tasks in target languages. |
| Outcome: | The proposed method outperforms supervised few-shot learning in LLMs of different sizes for translations between English and 13 Indic and 21 African low-resource languages. |
Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation (2023.emnlp-main)
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| Challenge: | Existing methods for question generation over knowledge bases rely on annotated data for fine-tuning . emergence of Large Language Models (LLMs) has shown impressive generalization ability in few-shot tasks. |
| Approach: | They propose to use a logical form to generate a question in a reasoning problem . they propose to extend the prompting method into a method that can generate questions in logical forms . |
| Outcome: | The proposed method outperforms baselines on three public KBQG datasets. |
Fine-tuning Large Language Models for Improving Factuality in Legal Question Answering (2025.coling-main)
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| Challenge: | Hallucination remains a critical challenge in large language models (LLMs) in high-stake domains such as legal question answering. |
| Approach: | They propose a method to mitigate hallucination in legal question answering by using behavior cloning and a novel Hard Sample-aware Direct Preference Optimization. |
| Outcome: | The proposed method improves non-hallucinated Statute Rate, Statute Relevance Rate, Legal Claim Truthfulness, and traditional metrics. |
Evaluating Parameter Efficient Learning for Generation (2022.emnlp-main)
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Peng Xu, Mostofa Patwary, Shrimai Prabhumoye, Virginia Adams, Ryan Prenger, Wei Ping, Nayeon Lee, Mohammad Shoeybi, Bryan Catanzaro
| Challenge: | Parameter efficient learning methods (PERMs) are gaining attention for their ability to adapt to a downstream task. |
| Approach: | They propose to use parameter efficient learning methods to improve model adaptation . they compare in-domain evaluations and generalizations to unseen domains and new datasets . |
| Outcome: | The proposed method outperforms finetuning and PERMs in in-domain evaluations. |
Mathematical Word Problem Generation from Commonsense Knowledge Graph and Equations (2021.emnlp-main)
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| Challenge: | Existing models for generating mathematical word problems are lacking in educational assessment. |
| Approach: | They propose an end-to-end neural model to generate diverse mathematical word problems from commonsense knowledge graph and equations. |
| Outcome: | The proposed model outperforms the SOTA models in terms of evaluation metrics and topic relevance. |
Abstractive Summarization of Bengali Academic Videos Based on Audio Subtitles (2026.findings-eacl)
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| Challenge: | Existing methods for summarizing educational videos in Bengali are limited due to the rapid growth of educational video content. |
| Approach: | They propose an end-to-end pipeline for the abstractive summarization of Bengali videos . they fine-tuned the BanglaT5 model on a new benchmark dataset . |
| Outcome: | The proposed system preprocesses audio and converts speech to text using Google's Speech Recognition API. |
Hurdles to Progress in Long-form Question Answering (2021.naacl-main)
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| Challenge: | Long-form question answering (LFQA) involves retrieving documents relevant to a given question and using them to generate a paragraph-length answer. |
| Approach: | They propose a long-form question answering system that relies on sparse attention and contrastive retriever learning to achieve state-of-the-art performance on the ELI5 LFQA dataset. |
| Outcome: | The proposed system tops the public leaderboard on the ELI5 LFQA dataset, but it has several troubling issues. |
Inference Scaling for Bridging Retrieval and Augmented Generation (2025.findings-naacl)
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| Challenge: | Existing work observed the generator bias, such that improving the retrieval results may negatively affect the outcome. |
| Approach: | They propose to use inference scaling to aggregate inference calls from the permuted order of retrieved contexts to create a new ranking. |
| Outcome: | The proposed approach improves ROUGE-L on MS MARCO and EM on HotpotQA benchmarks by 7 points. |
Element-aware Summarization with Large Language Models: Expert-aligned Evaluation and Chain-of-Thought Method (2023.acl-long)
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| Challenge: | Experimental results show that automatic summarization generates concise summaries that contain key ideas of source documents. |
| Approach: | They propose to use Element-aware test sets to annotate news-related reference summaries to focus on more fine-grained news elements objectively and comprehensively. |
| Outcome: | The proposed method outperforms state-of-the-art fine-tuned PLMs and zero-shot LLMs by +4.33/+4.77 on the two datasets, respectively. |
An Evaluation Framework for Legal Document Summarization (2022.lrec-1)
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| Challenge: | Existing metrics for summarizing legal documents fail to evaluate intent in the original text. |
| Approach: | They propose an automated intent-based summarization metric which shows a better agreement with human evaluation as compared to other automated metrics like BLEU, ROUGE-L etc. |
| Outcome: | The proposed method shows that human evaluation is more accurate than other metrics. |
Structured List-Grounded Question Answering (2025.coling-main)
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| Challenge: | Document-grounded dialogue systems aim to answer user queries by leveraging external information. |
| Approach: | They propose a dataset to evaluate QA systems' ability to interpret and use structured lists . they use language models and model-based filtering processes to enhance data quality . |
| Outcome: | The proposed model outperforms baselines on the LIST2QA dataset . it shows that the proposed model is more accurate and complete than baselines . |
Engagement-driven Persona Prompting for Rewriting News Tweets (2025.coling-main)
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| Challenge: | Text style transfer is a challenging research task which modifies the linguistic style of a text to meet pre-set objectives such as making the text simpler or more accessible. |
| Approach: | They propose to use large language models to rewrite Dutch news tweets to match specific linguistic styles to achieve a more accessible and accessible text. |
| Outcome: | The proposed prompting strategies perform best for rewriting Dutch news tweets in specific linguistic styles (formal, casual and factual). |
LLM aided semi-supervision for efficient Extractive Dialog Summarization (2023.findings-emnlp)
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| Challenge: | a method to extract dialog summarization data from unlabeled data is currently expensive to build. |
| Approach: | They propose a method to extract dialog summarization using unlabeled data . they frame summarizing as a question-answering problem and use pseudo-labels to fine-tune a chat summarisation model . |
| Outcome: | The proposed method achieves 65.9/57.0/61.0 ROUGE-1/-2/-L on a TWEETSUMM dataset compared with current state-of-the-art methods on the entire training dataset. |
Exploring In-Context Learning for Knowledge Grounded Dialog Generation (2023.findings-emnlp)
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| Challenge: | Existing knowledge grounded dialog generation models are prone to hallucination and produce factually inaccurate outputs. |
| Approach: | They propose a retrieval-based framework which leverages in-context learning and retrieval techniques to enhance LLMs on knowledge grounded dialog generation. |
| Outcome: | The proposed framework outperforms existing training-based models on a large-scale knowledge graph with 1M+ facts and is expected to perform knowledge-intensive tasks. |
Replace and Report: NLP Assisted Radiology Report Generation (2023.findings-acl)
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| Challenge: | Clinical practice frequently uses medical imaging for diagnosis and treatment. |
| Approach: | They propose a template-based approach to generate radiology reports from radiographs . they use multilabel image classifiers to generate tags, pathological descriptions from tags . |
| Outcome: | The proposed method improves on the most popular radiology report datasets. |
KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation (2020.emnlp-main)
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| Challenge: | Existing methods for data-to-text generation rely on labeled data, which is costly to acquire and limits their application to new tasks and domains. |
| Approach: | They propose to leverage pre-training and transfer learning to address this problem by leveraging a general knowledge-grounded generation model and a knowledge-based model. |
| Outcome: | The proposed model can generate knowledge-enriched text on a knowledge-grounded text corpus crawled from the web in three settings. |
FLRC: Fine-grained Low-Rank Compressor for Efficient LLM Inference (2025.emnlp-main)
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| Challenge: | Low-rank compression can reduce memory usage and computational demand, but results are poor during decoding. |
| Approach: | They propose a fine-grained low-rank compression algorithm that determines optimal rank allocation for each layer and incorporates progressive low-ranked decoding to maintain text generation quality. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on summarization tasks and on understanding tasks. |
Tox-BART: Leveraging Toxicity Attributes for Explanation Generation of Implicit Hate Speech (2024.findings-acl)
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| Challenge: | Existing language models to generate implicit hate explanations are lacking in many fields. |
| Approach: | They propose to use language models to generate explicit hate posts to make it clear . they find that simpler models incorporating external toxicity signals outperform KG-infused models . |
| Outcome: | The proposed setup produces more precise explanations than zero-shot GPT-3.5, highlighting the intricate nature of the task. |
Region-Grounded Report Generation for 3D Medical Imaging: A Fine-Grained Dataset and Graph-Enhanced Framework (2026.acl-long)
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Cong Huy Nguyen, Son Dinh Nguyen, Guanlin Li, Tuan Dung Nguyen, Aditya Narayan Sankaran, Mai Huy Thong, Thanh Trung Nguyen, Mai Hong Son, Reza Farahbakhsh, Phi Le Nguyen, Noel Crespi
| Challenge: | Current methods map whole volumes to reports, ignoring the clinical workflow of analyzing localized Regions of Interest (RoIs) Current models exhibit suboptimal accuracy and are prone to significant hallucinations. |
| Approach: | They propose a framework that mimics the professional radiologist diagnostic workflow by employing graph-based relational modules to capture dependencies between RoI attributes. |
| Outcome: | The proposed framework surpasses existing models by 19.7% in BLEU and 4.7% in ROUGE-L while achieving a 45.8% improvement in clinical metrics. |
Rethinking Sign Language Translation: The Impact of Signer Dependence on Model Evaluation (2025.findings-emnlp)
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| Challenge: | Sign Language Translation evaluations remain largely signer-dependent, with overlapping signers across train/dev/test. |
| Approach: | We conduct signer-fold cross-validation on three leading SLT models . they find that under signer independent evaluation performance drops sharply . |
| Outcome: | a signer-dependent evaluation can substantially overestimate SLT capability, the authors say . they recommend adopting signer independent protocols to ensure generalisation to unseen signers . |
MATCHA: Matching Text via Contrastive Semantic Alignment (2026.findings-acl)
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| Challenge: | MATCHA is an automatic metric that rewards semantic agreement with a reference and penalizes contradictions. |
| Approach: | They introduce a metric that jointly rewards semantic agreement with a reference and penalizes contradictions. |
| Outcome: | The proposed metric outperforms popular metrics on eight public benchmarks compared with human annotations on question-answering, image caption generation, natural language inference, summarization, and semantic textual similarity tasks. |
Mixture of Weight-shared Heterogeneous Group Attention Experts for Dynamic Token-wise KV Optimization (2025.emnlp-main)
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| Challenge: | Existing methods for token-level KV optimization and grouping of tokens are inefficient and strain compute and storage resources. |
| Approach: | They propose a mixture-of-expert approach that dynamically optimizes token-wise computation and memory allocation by a token-based expert-choice routing mechanism guided by learned importance scores. |
| Outcome: | The proposed approach retains all tokens while adaptively routing them to specialized experts with varying KV group sizes, balancing granularity and efficiency. |
Personalized LLM Decoding via Contrasting Personal Preference (2025.emnlp-main)
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| Challenge: | Personalization of large language models (LLMs) is becoming increasingly important as they are increasingly deployed in real-world applications. |
| Approach: | They propose a decoding-time approach that leverages the user's implicit reward signal by performing parameter-efficient fine-tuning on user-specific data. |
| Outcome: | The proposed approach improves personalization by an average of 10.57% in ROUGE-L without external reward models or additional training procedures. |
REMIND: Memorization and Unlearning in LLMs Through the Lens of Input Loss Landscapes (2026.acl-long)
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| Challenge: | REMIND is a framework that diagnoses residual memorization states by probing local ILL curvature over semantically coherent neighborhoods. |
| Approach: | They propose a framework that diagnoses memorization states by probing local ILL curvature over semantically coherent neighborhoods. |
| Outcome: | The proposed framework outperforms baseline models with 82% multi-class ROC-AUC and 2 higher AUC at 1% FPR. |