Papers with extractive summarization
NLP for Conversations: Sentiment, Summarization, and Group Dynamics (C18-3)
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| Challenge: | a tutorial focuses on computational models for conversational structure, summarization and sentiment detection, and group dynamics. |
| Approach: | a tutorial will provide examples of specific NLP tasks for conversational structure, summarization and sentiment detection, and group dynamics. |
| Outcome: | The tutorial focuses on the three areas of conversational structure, summarization and sentiment detection, and group dynamics. |
ExtraPhrase: Efficient Data Augmentation for Abstractive Summarization (2022.naacl-srw)
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| Challenge: | Recent studies indicated that neural methods are governed by the scaling law for the amount of training data. |
| Approach: | They propose a low-cost strategy to augment training data for abstractive summarization tasks by extracting summarized text and paraphrasing it. |
| Outcome: | The proposed strategy outperforms back-translation and self-training and is more cost-efficient when training data is small. |
Extractive Summarization with Text Generator (2024.naacl-long)
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| Challenge: | Existing extractive systems lack gold training signals, thereby hindering learning of extractive models. |
| Approach: | They propose to use text generators to train extractive summarizers by approximating outputs of abstractive summaries. |
| Outcome: | The proposed method can be used to train extractive summarizers without training . it is shown that the approximated summaries correlate positively with the auxiliary summary outputs. |
Multi-Document Summarization with Determinantal Point Processes and Contextualized Representations (D19-54)
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| Challenge: | Determinantal point processes (DPP) is one of the best performing techniques for extractive summarization. |
| Approach: | They propose to combine determinantal point processes with surface indicators for effective identification of summary-worthy sentences. |
| Outcome: | The determinantal point processes (DPP) framework is one of the best performing in summarization competitions. |
Extractive Summarization with SWAP-NET: Sentences and Words from Alternating Pointer Networks (P18-1)
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| Challenge: | Abstractive summarization methods use factual and grammatical errors to generate summaries. |
| Approach: | They propose a neural sequence-to-sequence model for extractive summarization called SWAP-NET . it identifies salient sentences and key words in an input document and combines them to form an extractive summary. |
| Outcome: | The proposed model outperforms state-of-the-art extractive summarization methods on large scale corpora. |
Reinforced Extractive Summarization with Question-Focused Rewards (P18-3)
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| Challenge: | Existing methods for extractive summarization use human abstracts to create annotations for extraction units. |
| Approach: | They propose a method where abstracts are converted to Cloze-style comprehension questions to generate extractive summarization. |
| Outcome: | The proposed method surpasses state-of-the-art systems on the standard summarization dataset. |
Exploiting Discourse-Level Segmentation for Extractive Summarization (D19-54)
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| Challenge: | Existing approaches to extract summarize text are based on sentences as the elementary unit, but semantic segments containing supplementary information or descriptive details are often nonessential in the generated summaries. |
| Approach: | They propose to exploit discourse-level segmentation as a finer-grained means to more precisely pinpoint the core content in a document. |
| Outcome: | The proposed method improves extractive summarization performance on CNN/Daily Mail dataset. |
Abstractive Summarizers are Excellent Extractive Summarizers (2023.acl-short)
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| Challenge: | Abstractive summarization systems have traditionally been fragmented, limiting the benefits of compatible models. |
| Approach: | They propose three new inference algorithms using sequence-to-sequence architectures to model extractive summarization with an abstractive summmarization system. |
| Outcome: | The proposed algorithms outperform existing models on CNN and Dailymail and show that they are more efficient than existing models. |
Identifying Implicit Quotes for Unsupervised Extractive Summarization of Conversations (2020.aacl-main)
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| Challenge: | Existing methods of unsupervised summarization are lacking. |
| Approach: | They propose an unsupervised unsupervised extractive neural summarization model that extracts quotes as summaries from conversational texts. |
| Outcome: | The proposed model can extract quoted sentences as summaries from two email and social media datasets. |
Neural Label Search for Zero-Shot Multi-Lingual Extractive Summarization (2022.acl-long)
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| Challenge: | Existing methods to translate sentences to other languages using heuristics are challenging. |
| Approach: | They propose a model that learns hierarchical weights for different sets of labels and applies them to other languages to translate them. |
| Outcome: | The proposed model can translate English datasets to other languages and obtain different sets of labels again using heuristics. |
Hierarchical Attention Graph for Scientific Document Summarization in Global and Local Level (2024.findings-naacl)
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| Challenge: | Existing methods for document summarization focus on one type of relation, neglecting the simultaneous effective modeling of both relations. |
| Approach: | They propose a graph neural network-based approach to local and global document summarization using hierarchical discourses. |
| Outcome: | The proposed approach improves on two benchmark datasets and shows that hierarchical structures are important for document summarization. |
Searching for Effective Neural Extractive Summarization: What Works and What’s Next (P19-1)
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| Challenge: | Recent years have seen success in the use of deep neural networks on text summarization, but there is no clear understanding of why they perform so well or how they might be improved. |
| Approach: | They propose to use different types of model architectures to improve extractive summarization systems. |
| Outcome: | The proposed framework achieves state-of-the-art on CNN/DailyMail by a large margin based on observations and analysis. |
HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information (2022.findings-acl)
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| Challenge: | Existing models that treat texts as linear sequences do not include hierarchical structure information. |
| Approach: | They propose to inject hierarchical structure information into an extractive summarization model by combining hierarchically structured text with a pre-trained Transformer language model. |
| Outcome: | The proposed model outperforms a baseline model on PubMed and arXiv datasets and the hierarchical structure information is not injected. |
Differentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report Summarization (2022.acl-long)
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| Challenge: | Prior research on radiology report summarization has focused on single-step end-to-end models which subsume the task of salient content acquisition. |
| Approach: | They propose a two-step extractive summarization followed by abstractive summaries and a new method that breaks down the extractive part into two independent tasks: extraction of salient (1) sentences and (2) keywords. |
| Outcome: | The proposed model improves on English radiology reports with an overall improvement in F1 score of 3-4% compared to single-step and two-step-with-single-extractive-process baselines. |
Extractive is not Faithful: An Investigation of Broad Unfaithfulness Problems in Extractive Summarization (2023.acl-long)
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| Challenge: | Abstractive summarization is less prone to unfaithfulness issues than abstractive summaries . but, unfaitfulness problems, i.e., hallucinating new information, are still a problem in extractive summarisation . |
| Approach: | They propose a typology with five types of broad unfaithfulness problems that can appear in extractive summaries, including and beyond not-entailment. |
| Outcome: | The proposed metric shows that it detects unfaithful summaries faster than existing faithfulness evaluation metrics. |
Exploring Multitask Learning for Low-Resource Abstractive Summarization (2021.findings-emnlp)
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| Challenge: | Recent work shows that training text encoders using data from multiple tasks helps to produce an encoder that can be used in numerous downstream tasks with minimal fine-tuning. |
| Approach: | They incorporate four different tasks to improve abstractive summarization performance . they use a pretrained BERT model and train all tasks using a small-scale training corpus . |
| Outcome: | The proposed model outperforms a model trained in a multitask setting with no additional summarization data. |
MultiHumES: Multilingual Humanitarian Dataset for Extractive Summarization (2021.eacl-main)
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| Challenge: | a new multilingual summarization model is being developed to help humanitarian experts process large amounts of secondary data to derive situational awareness and guide decision-making. |
| Approach: | They propose to use multilingual documents and annotated snippets to improve extraction of secondary data for humanitarian response experts. |
| Outcome: | The proposed model provides multilingual documents with informative snippets that have been annotated by humanitarian analysts over the past four years. |
Provable Fast Greedy Compressive Summarization with Any Monotone Submodular Function (N18-1)
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| Challenge: | Submodular maximization with the greedy algorithm is an effective approach to extractive summarization. |
| Approach: | They propose a submodular maximization method that is 100 to 400 times faster than existing methods for extractive summarization. |
| Outcome: | The proposed method is 100 to 400 times faster than existing method based on integer-linear-programming formulations and achieves 95%-approximation. |
Unsupervised Extractive Summarization using Pointwise Mutual Information (2021.eacl-main)
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| Challenge: | Unsupervised approaches to extractive summarization rely on notion of sentence importance defined by semantic similarity between a sentence and the document. |
| Approach: | They propose a method to measure relevance and redundancy using PMI between sentences. |
| Outcome: | The proposed method outperforms similarity-based methods on news, medical journal articles, and personal anecdotes. |
Self-Supervised Learning for Contextualized Extractive Summarization (P19-1)
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| Challenge: | Existing models for extractive summarization are usually trained from scratch with a cross-entropy loss . previous work builds an end-to-end system to learn to choose sentences without explicitly modeling document context . |
| Approach: | They propose three auxiliary pre-training tasks that learn to capture the document context in a self-supervised fashion. |
| Outcome: | The proposed models outperform existing models on a CNN/DM dataset. |
Extractive Summarization via ChatGPT for Faithful Summary Generation (2023.findings-emnlp)
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| Challenge: | Abstractive summarization methods struggle with generating ungrammatical or even nonfactual contents. |
| Approach: | They evaluate ChatGPT's performance on extractive summarization and compare it with traditional fine-tuning methods on benchmark datasets. |
| Outcome: | The proposed pipeline performs better than abstractive methods on summary faithfulness and in-context learning. |
Constrained Regeneration for Cross-Lingual Query-Focused Extractive Summarization (2022.coling-1)
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Elsbeth Turcan, David Wan, Faisal Ladhak, Petra Galuscakova, Sukanta Sen, Svetlana Tchistiakova, Weijia Xu, Marine Carpuat, Kenneth Heafield, Douglas Oard, Kathleen McKeown
| Challenge: | Query-focused summarization of foreign-language documents can help a user understand whether a document is relevant to a query term. |
| Approach: | They propose to use machine translation and post-editing to improve human relevance judgments . they include a query term in a summary when its translation appears in the source document . |
| Outcome: | The proposed approach improves human relevance judgments by including a query term in a summary when its translation appears in the source document. |
Extractive Summarization Considering Discourse and Coreference Relations based on Heterogeneous Graph (2021.eacl-main)
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| Challenge: | Abstractive summarization aims to select salient text spans (mostly sentences) from the input document. |
| Approach: | They propose a heterogeneous graph based model that incorporates both discourse and coreference relations between text spans of different granularity. |
| Outcome: | The proposed model is efficient and factually reliable on a benchmark summarization dataset. |
Context-Aware Hierarchical Merging for Long Document Summarization (2025.findings-acl)
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| Challenge: | Hierarchical merging is a technique used to summarize very long texts . it can amplify LLM hallucinations, increasing the risk of factual inaccuracies . |
| Approach: | They propose to enrich hierarchical merging with context from the source document to reduce the risk of factual inaccuracies. |
| Outcome: | The proposed methods outperform zero-shot and hierarchical merging baselines on legal and narrative datasets. |
A Set Prediction Network For Extractive Summarization (2023.findings-acl)
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| Challenge: | Recent approaches to extracting salient sentences from source document are naive and lack dependencies between sentences. |
| Approach: | They propose a set prediction network to detect redundancy relationship between sentences . they use a non-autoregressive decoder to predict sentences in parallel . |
| Outcome: | The proposed method outperforms previous state-of-the-art models on extracted summary datasets. |
Reading Like HER: Human Reading Inspired Extractive Summarization (D19-1)
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| Challenge: | Existing methods for extracting text summarization are abstractive and extractive. |
| Approach: | They propose a novel approach for extractive summarization by simulating two stages . they adopt a convolutional neural network to encode gist of paragraphs for rough reading . |
| Outcome: | The proposed method significantly outperforms the state-of-the-art extractive methods on CNN and DailyMail datasets. |
Stepwise Extractive Summarization and Planning with Structured Transformers (2020.emnlp-main)
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| Challenge: | Existing approaches to extractive summarization use transformers to learn the structure of long inputs. |
| Approach: | They propose encoder-centric stepwise models for extractive summarization using structured transformers – HiBERT and Extended Transformers . |
| Outcome: | The proposed models outperform previous models on CNN/DailyMail extractive summarization and Rotowire table-to-text generation. |
Adversarial Semantic Collisions (2020.emnlp-main)
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| Challenge: | Existing approaches to generate semantic collisions for NLP tasks are vulnerable to adversarial examples. |
| Approach: | They propose gradient-based approaches for generating semantic collisions given white-box access to a model and deploy them against several NLP tasks. |
| Outcome: | The proposed approaches evade perplexity-based filtering and discuss other potential mitigations. |
Jointly Extracting and Compressing Documents with Summary State Representations (N19-1)
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| Challenge: | Text summarization is an important NLP problem with a wide range of applications in data-driven industries. |
| Approach: | They propose a neural model that extracts sentences from a document and compresses them. |
| Outcome: | The proposed model generates concise and informa-tive summaries on CNN/DailyMail and Newsroom datasets and human evaluations show it outperforms existing methods. |
BanditSum: Extractive Summarization as a Contextual Bandit (D18-1)
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| Challenge: | Existing methods for extractive summarization are heuristically generated and require a set of binary labels to be selected. |
| Approach: | They propose a method for training neural networks to perform single-document extractive summarization without heuristically-generated extractive labels. |
| Outcome: | The proposed method achieves better ROUGE scores than the state-of-the-art methods and significantly fewer update steps than competing approaches. |
Facet-Aware Evaluation for Extractive Summarization (2020.acl-main)
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| Challenge: | lexical overlap is a common evaluation metric for extractive summarization, but recent studies reveal its limitations. |
| Approach: | They propose a facet-aware evaluation setup for better assessment of information coverage in extractive summaries. |
| Outcome: | The proposed evaluation setup improves human correlation with extractive summarization datasets and improves comparative analysis. |
Exploring Content Selection in Summarization of Novel Chapters (2020.acl-main)
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| Challenge: | We focus on extractive summarization, which requires the creation of a gold-standard set of extractive summary summaries. |
| Approach: | They propose a new metric for aligning summary sentences with chapter sentences to create gold extracts. |
| Outcome: | The proposed method improves on previous methods and automatic metrics and a crowd-sourced pyramid analysis. |
Separating Context and Pattern: Learning Disentangled Sentence Representations for Low-Resource Extractive Summarization (2023.findings-acl)
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| Challenge: | Context information is one of the key factors for extractive summarization, but other factors can be used to identify sentence importance. |
| Approach: | They propose to disentangle context and pattern factors for extractive summarization . they separate context and patterns for a better generalization ability in low-resource setting . |
| Outcome: | The proposed model can be used in the zero-shot setting or fine-tuned in the few-shot settings. |
At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization (2020.coling-main)
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| Challenge: | Existing studies have shown that extracting sentences at sentence level is not the best solution for document summarization. |
| Approach: | They propose to extract sub-sentential units based on the constituency parsing tree and a neural extractive model which leverages the sub-sensential information and extracts them. |
| Outcome: | The proposed model performs competitively compared to full sentence extraction under automatic and human evaluations. |
Fact-level Extractive Summarization with Hierarchical Graph Mask on BERT (2020.coling-main)
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| Challenge: | Existing extractive summarization models generate summaries by selecting salient sentences, but there is a gap between the human-written gold summary and oracle sentence labels. |
| Approach: | They propose to extract fact-level semantic units for better extractive summarization by incorporating a hierarchical structure into the model and incorporate it with BERT using a Hierarchical graph mask. |
| Outcome: | The proposed model achieves state-of-the-art on the CNN/DaliyMail dataset. |
Heterogeneous Graph Neural Networks for Extractive Document Summarization (2020.acl-main)
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| Challenge: | Existing models capture cross-sentence relations with recurrent neural networks, but they are hard to capture sentence-level long-distance dependency. |
| Approach: | They propose a graph-based neural network for extractive summarization which contains semantic nodes apart from sentences. |
| Outcome: | The proposed graph-based neural network is the first to incorporate different types of nodes into it and perform a qualitative analysis. |
Document-level Claim Extraction and Decontextualisation for Fact-Checking (2024.acl-long)
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| Challenge: | Existing methods for document-level claim extraction focus on identifying and extracting claims from individual sentences. |
| Approach: | They propose a method for document-level claim extraction for fact-checking which aims to extract check-worthy claims from documents and decontextualise them so they can be understood out of context. |
| Outcome: | The proposed method extracts check-worthy claims from documents and decontextualises them so they can be understood out of context. |
From News to Summaries: Building a Hungarian Corpus for Extractive and Abstractive Summarization (2024.lrec-main)
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| Challenge: | Existing models and datasets for training summarization models are limited for less resourceful languages like Hungarian . |
| Approach: | They propose to use a Hungarian corpus for training abstractive and extractive summarization models by cleaning, preprocessing and deduplication. |
| Outcome: | The proposed model trains abstractive and extractive summarization models using the dataset . it will be made publicly available, encouraging replication, further research, and real-world applications across various domains. |
HEGEL: Hypergraph Transformer for Long Document Summarization (2022.emnlp-main)
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| Challenge: | Abstract: Extractive summarization for long documents is challenging due to the extended structured input context. |
| Approach: | They propose a hypergraph neural network for extractive summarization by capturing cross-sentence relations. |
| Outcome: | The proposed model can capture cross-sentence relations and latent topics and keywords coreference, and section structure, and can be applied to scientific papers. |
Human Guided Exploitation of Interpretable Attention Patterns in Summarization and Topic Segmentation (2022.emnlp-main)
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| Challenge: | Existing studies have investigated the multi-head self-attention mechanism of transformers. |
| Approach: | They propose to use a human-in-the-loop pipeline to discover task-specific attention patterns and inject them into transformer models to improve their accuracy. |
| Outcome: | The proposed methods improve the performance of transformer models by incorporating predefined patterns into their attention matrices. |
DiffuSum: Generation Enhanced Extractive Summarization with Diffusion (2023.findings-acl)
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| Challenge: | Existing methods for extractive summarization are formulated as a sequence labeling problem by making individual sentence label predictions. |
| Approach: | They propose a novel paradigm for extractive summarization by directly generating summary sentences with diffusion models and extracting sentences based on sentence representation matching. |
| Outcome: | The proposed framework achieves state-of-the-art extractive results on CNN/DailyMail with ROUGE scores of 44.83/22.56/40.56. |
Multi-label Sequential Sentence Classification via Large Language Model (2024.findings-emnlp)
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| Challenge: | Existing approaches to sequential sentence classification are constrained by model size, sequence length, and single-label setting. |
| Approach: | They propose a large language model-based framework for both single- and multi-label SSC tasks that generate SSC labels through designed prompts. |
| Outcome: | The proposed framework enhances task understanding by incorporating demonstrations and a query to describe the prediction target. |
Towards Enhancing Coherence in Extractive Summarization: Dataset and Experiments with LLMs (2024.emnlp-main)
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| Challenge: | Existing methods for extractive summarization lack coherence, despite improvements . a human-annotated dataset is used to improve coherency of extractive summary . |
| Approach: | They propose to use human-annotated datasets to create coherent extractive summaries . they use supervised fine-tuning and natural language user feedback to enhance coherence . |
| Outcome: | The proposed dataset shows that LLMs can produce coherent summaries with human feedback. |
Title-based Extractive Summarization via MRC Framework (2024.lrec-main)
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| Challenge: | Existing studies on extractive summarization focus on scoring and selecting summary sentences . existing models tend to select generalized sentences while overlooking the overall content of a document. |
| Approach: | They propose a machine reading comprehension framework for extractive summarization by setting a query as the title. |
| Outcome: | The proposed framework outperforms existing models on long and short summaries in Korean and English . it can consider the semantic coherence and relevance of summary sentences in relation to the overall content . |
Plutus: Benchmarking Large Language Models in Low-Resource Greek Finance (2025.emnlp-main)
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Xueqing Peng, Triantafillos Papadopoulos, Efstathia Soufleri, Polydoros Giannouris, Ruoyu Xiang, Yan Wang, Lingfei Qian, Jimin Huang, Qianqian Xie, Sophia Ananiadou
| Challenge: | Greek is the dominant language of the world's merchant navy and is a key language for international trade. |
| Approach: | They propose to develop a Greek financial evaluation benchmark and a financial LLM fine-tuned on Greek-specific financial data to bridge this gap. |
| Outcome: | The proposed benchmarks surpass GPT-4 by 8.33%, GPT- 4o by 26.83%, and Deepseek-V3 by 67.74%. |
SAHM: A Benchmark for Arabic Financial and Shari’ah-Compliant Reasoning (2026.acl-long)
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Rania Elbadry, Sarfraz Ahmad, Ahmed Heakl, Dani Bouch, Momina Ahsan, Muhra AlMahri, Marwa Elsaid Khalil, Yuxia Wang, Salem Lahlou, Sophia Ananiadou, Veselin Stoyanov, Jimin Huang, Xueqing Peng, Preslav Nakov, Zhuohan Xie
| Challenge: | English financial NLP has progressed rapidly through benchmarks for sentiment, document understanding, and financial question answering. |
| Approach: | They propose a document-grounded benchmark and instruction-tuning dataset for Arabic financial NLP and Shari’ah-compliant reasoning. |
| Outcome: | The proposed dataset contains 14,380 expert-verified instances spanning seven tasks . it includes financial sentiment analysis, extractive summarization, and event–cause reasoning . |