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Neural Document Summarization by Jointly Learning to Score and Select Sentences

arXiv 1807.02305 ↗First posted 6 Jul 2018cs.CLcited by 56

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Abstract (arXiv)

Sentence scoring and sentence selection are two main steps in extractive document summarization systems. However, previous works treat them as two separated subtasks. In this paper, we present a novel end-to-end neural network framework for extractive document summarization by jointly learning to score and select sentences. It first reads the document sentences with a hierarchical encoder to obtain the representation of sentences. Then it builds the output summary by extracting sentences one by one. Different from previous methods, our approach integrates the selection strategy into the scoring model, which directly predicts the relative importance given previously selected sentences. Experiments on the CNN/Daily Mail dataset show that the proposed framework significantly outperforms the state-of-the-art extractive summarization models.

Key facts. Cited by 56 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jul 2018.

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Citing papers per year: 2021: 20; 2022: 12; 2023: 12; 2024: 5; 2025: 7.

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  1. 2021BARTScore: Evaluating Generated Text as Text Generationcited by 719
  2. 2022Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectorscited by 103
  3. 2024A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methodscited by 101
  4. 2023Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarizationcited by 70
  5. 2023From Sparse to Dense: GPT-4 Summarization with Chain of Density Promptingcited by 52
  6. 2024A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Modelscited by 52
  7. 2021A Survey on Dialogue Summarization: Recent Advances and New Frontierscited by 51
  8. 2021Automatic Text Evaluation through the Lens of Wasserstein Barycenterscited by 38
  9. 2021What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarizationcited by 35
  10. 2021InfoLM: A New Metric to Evaluate Summarization & Data2Text Generationcited by 33
  11. 2021Bidimensional Leaderboards: Generate and Evaluate Language Hand in Handcited by 32
  12. 2022Extractive is not Faithful: An Investigation of Broad Unfaithfulness Problems in Extractive Summarizationcited by 32
38 more
  1. 2022ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generationcited by 26
  2. 2022Spurious Correlations in Reference-Free Evaluation of Text Generationcited by 26
  3. 2023DiffuSum: Generation Enhanced Extractive Summarization with Diffusioncited by 25
  4. 2022AraBART: a Pretrained Arabic Sequence-to-Sequence Model for Abstractive Summarizationcited by 24
  5. 2021MemSum: Extractive Summarization of Long Documents Using Multi-Step Episodic Markov Decision Processescited by 19
  6. 2021Multiplex Graph Neural Network for Extractive Text Summarizationcited by 19
  7. 2021See, Hear, Read: Leveraging Multimodality with Guided Attention for Abstractive Text Summarizationcited by 18
  8. 2021A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regionscited by 17
  9. 2023NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklistcited by 16
  10. 2022An Extractive-and-Abstractive Framework for Source Code Summarizationcited by 15
  11. 2021HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarizationcited by 13
  12. 2021Neural Content Extraction for Poster Generation of Scientific Paperscited by 9
  13. 2022GoSum: Extractive Summarization of Long Documents by Reinforcement Learning and Graph Organized discourse statecited by 9
  14. 2023Generating EDU Extracts for Plan-Guided Summary Re-Rankingcited by 9
  15. 2023Contrastive Hierarchical Discourse Graph for Scientific Document Summarizationcited by 9
  16. 2022HumSet: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crisis Responsecited by 7
  17. 2025An Evaluation of Large Language Models on Text Summarization Tasks Using Prompt Engineering Techniquescited by 6
  18. 2021Improving Stack Overflow question title generation with copying enhanced CodeBERT model and bi-modal informationcited by 5
  19. 2021CNewSum: A Large-scale Chinese News Summarization Dataset with Human-annotated Adequacy and Deducibility Levelcited by 5
  20. 2022Post-Training Dialogue Summarization using Pseudo-Paraphrasingcited by 4
  21. 2022CLTS+: A New Chinese Long Text Summarization Dataset with Abstractive Summariescited by 4
  22. 2023A comprehensive review of automatic text summarization techniques: method, data, evaluation and codingcited by 4
  23. 2025PIS: Linking Importance Sampling and Attention Mechanisms for Efficient Prompt Compressioncited by 4
  24. 2021Improving Online Forums Summarization via Hierarchical Unified Deep Neural Networkcited by 2
  25. 2022Differentiable N-gram Objective on Abstractive Summarizationcited by 2
  26. 2022Universal Evasion Attacks on Summarization Scoringcited by 2
  27. 2023Low Resource Summarization using Pre-trained Language Modelscited by 2
  28. 2024CTISum: A New Benchmark Dataset For Cyber Threat Intelligence Summarizationcited by 2
  29. 2021KnowGraph@IITK at SemEval-2021 Task 11: Building KnowledgeGraph for NLP Researchcited by 1
  30. 2021A Condense-then-Select Strategy for Text Summarizationcited by 1
  31. 2023HunSum-1: an Abstractive Summarization Dataset for Hungariancited by 1
  32. 2023Bipartite Graph Pre-training for Unsupervised Extractive Summarization with Graph Convolutional Auto-Encoderscited by 1
  33. 2023Unsupervised Extractive Summarization with Learnable Length Control Strategiescited by 1
  34. 2024Reward-based Input Construction for Cross-document Relation Extractioncited by 1
  35. 2021Centrality Meets Centroid: A Graph-based Approach for Unsupervised Document Summarizationcited by 0
  36. 2021Graph Neural Networks for Natural Language Processing: A Surveycited by 0
  37. 2021Borrowing from Similar Code: A Deep Learning NLP-Based Approach for Log Statement Automationcited by 0
  38. 2023Inducing Causal Structure for Abstractive Text Summarizationcited by 0

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