mlsanityData to 7 Oct 2026

Neural Latent Extractive Document Summarization

arXiv 1808.07187 ↗First posted 22 Aug 2018cs.CL cs.AI cs.LGcited by 20

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Abstract (arXiv)

Extractive summarization models require sentence-level labels, which are usually created heuristically (e.g., with rule-based methods) given that most summarization datasets only have document-summary pairs. Since these labels might be suboptimal, we propose a latent variable extractive model where sentences are viewed as latent variables and sentences with activated variables are used to infer gold summaries. During training the loss comes directly from gold summaries. Experiments on the CNN/Dailymail dataset show that our model improves over a strong extractive baseline trained on heuristically approximated labels and also performs competitively to several recent models.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Aug 2018.

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Citing papers per year: 2021: 7; 2022: 3; 2023: 3; 2024: 4; 2025: 3.

20 papers.

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  1. 2024A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Modelscited by 52
  2. 2021Bidimensional Leaderboards: Generate and Evaluate Language Hand in Handcited by 32
  3. 2021Controllable Abstractive Dialogue Summarization with Sketch Supervisioncited by 27
  4. 2021Topic-Aware Contrastive Learning for Abstractive Dialogue Summarizationcited by 23
  5. 2021Multiplex Graph Neural Network for Extractive Text Summarizationcited by 19
  6. 2021Topic-Guided Abstractive Multi-Document Summarizationcited by 19
  7. 2021Hierarchical Cross-Modality Semantic Correlation Learning Model for Multimodal Summarizationcited by 18
  8. 2023NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklistcited by 16
  9. 2022Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarizationcited by 14
  10. 2025Evaluating Small Language Models for News Summarization: Implications and Factors Influencing Performancecited by 12
  11. 2024The Power of Summary-Source Alignmentscited by 7
  12. 2021Towards objectively evaluating the quality of generated medical summariescited by 5
8 more
  1. 2022LR-Sum: Summarization for Less-Resourced Languagescited by 5
  2. 2023A comprehensive review of automatic text summarization techniques: method, data, evaluation and codingcited by 4
  3. 2024Understanding Position Bias Effects on Fairness in Social Multi-Document Summarizationcited by 4
  4. 2022Universal Evasion Attacks on Summarization Scoringcited by 2
  5. 2025Efficient Extractive Text Summarization for Online News Articles Using Machine Learningcited by 1
  6. 2023Efficient and Interpretable Compressive Text Summarisation with Unsupervised Dual-Agent Reinforcement Learningcited by 0
  7. 2024Extractive text summarisation of Privacy Policy documents using machine learning approachescited by 0
  8. 2025OrderSum: Semantic Sentence Ordering for Extractive Summarizationcited by 0