# Neural Latent Extractive Document Summarization

arXiv: https://arxiv.org/abs/1808.07187 · HTML: https://mlsanity.com/paper/1808.07187/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2018.

## Built on by (most cited first, 20 of 20)

- [A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models](https://mlsanity.com/paper/2406.11289/) (2024)
- [Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand](https://mlsanity.com/paper/2112.04139/) (2021)
- [Controllable Abstractive Dialogue Summarization with Sketch Supervision](https://mlsanity.com/paper/2105.14064/) (2021)
- [Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization](https://mlsanity.com/paper/2109.04994/) (2021)
- [Multiplex Graph Neural Network for Extractive Text Summarization](https://mlsanity.com/paper/2108.12870/) (2021)
- [Topic-Guided Abstractive Multi-Document Summarization](https://mlsanity.com/paper/2110.11207/) (2021)
- [Hierarchical Cross-Modality Semantic Correlation Learning Model for Multimodal Summarization](https://mlsanity.com/paper/2112.12072/) (2021)
- [NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist](https://mlsanity.com/paper/2305.08566/) (2023)
- [Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization](https://mlsanity.com/paper/2209.00930/) (2022)
- [Evaluating Small Language Models for News Summarization: Implications and Factors Influencing Performance](https://mlsanity.com/paper/2502.00641/) (2025)
- [The Power of Summary-Source Alignments](https://mlsanity.com/paper/2406.00842/) (2024)
- [Towards objectively evaluating the quality of generated medical summaries](https://mlsanity.com/paper/2104.04412/) (2021)
- [LR-Sum: Summarization for Less-Resourced Languages](https://mlsanity.com/paper/2212.09674/) (2022)
- [A comprehensive review of automatic text summarization techniques: method, data, evaluation and coding](https://mlsanity.com/paper/2301.03403/) (2023)
- [Understanding Position Bias Effects on Fairness in Social Multi-Document Summarization](https://mlsanity.com/paper/2405.01790/) (2024)
- [Universal Evasion Attacks on Summarization Scoring](https://mlsanity.com/paper/2210.14260/) (2022)
- [Efficient Extractive Text Summarization for Online News Articles Using Machine Learning](https://mlsanity.com/paper/2509.15614/) (2025)
- [Efficient and Interpretable Compressive Text Summarisation with Unsupervised Dual-Agent Reinforcement Learning](https://mlsanity.com/paper/2306.03415/) (2023)
- [Extractive text summarisation of Privacy Policy documents using machine learning approaches](https://mlsanity.com/paper/2404.08686/) (2024)
- [OrderSum: Semantic Sentence Ordering for Extractive Summarization](https://mlsanity.com/paper/2502.16180/) (2025)

## 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.