# Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints

arXiv: https://arxiv.org/abs/1603.08887 · HTML: https://mlsanity.com/paper/1603.08887/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2016.

## Built on by (most cited first, 30 of 34)

- [Annotating and Modeling Fine-grained Factuality in Summarization](https://mlsanity.com/paper/2104.04302/) (2021)
- [ASPECTNEWS: Aspect-Oriented Summarization of News Documents](https://mlsanity.com/paper/2110.08296/) (2021)
- [Sequence Level Contrastive Learning for Text Summarization](https://mlsanity.com/paper/2109.03481/) (2021)
- [HIBRIDS: Attention with Hierarchical Biases for Structure-aware Long Document Summarization](https://mlsanity.com/paper/2203.10741/) (2022)
- [Straight to the Gradient: Learning to Use Novel Tokens for Neural Text Generation](https://mlsanity.com/paper/2106.07207/) (2021)
- [Prompt-Based Length Controlled Generation with Multiple Control Types](https://mlsanity.com/paper/2406.10278/) (2024)
- [End-to-End Segmentation-based News Summarization](https://mlsanity.com/paper/2110.07850/) (2021)
- [Attention Temperature Matters in Abstractive Summarization Distillation](https://mlsanity.com/paper/2106.03441/) (2021)
- [Prompt-Based Length Controlled Generation with Reinforcement Learning](https://mlsanity.com/paper/2308.12030/) (2023)
- [Discourse Analysis via Questions and Answers: Parsing Dependency Structures of Questions Under Discussion](https://mlsanity.com/paper/2210.05905/) (2022)
- [Hierarchical3D Adapters for Long Video-to-text Summarization](https://mlsanity.com/paper/2210.04829/) (2022)
- [Contextualized Rewriting for Text Summarization](https://mlsanity.com/paper/2102.00385/) (2021)
- [Concise Answers to Complex Questions: Summarization of Long-form Answers](https://mlsanity.com/paper/2305.19271/) (2023)
- [Latent Prompt Tuning for Text Summarization](https://mlsanity.com/paper/2211.01837/) (2022)
- [QUDSELECT: Selective Decoding for Questions Under Discussion Parsing](https://mlsanity.com/paper/2408.01046/) (2024)
- [Probing LLMs for Multilingual Discourse Generalization Through a Unified Label Set](https://mlsanity.com/paper/2503.10515/) (2025)
- [Evaluation of Summarization Systems across Gender, Age, and Race](https://mlsanity.com/paper/2110.04384/) (2021)
- [A General Contextualized Rewriting Framework for Text Summarization](https://mlsanity.com/paper/2207.05948/) (2022)
- [Improving Graph-Based Text Representations with Character and Word Level N-grams](https://mlsanity.com/paper/2210.05999/) (2022)
- [Follow-on Question Suggestion via Voice Hints for Voice Assistants](https://mlsanity.com/paper/2310.17034/) (2023)
- [Consistency Evaluation of News Article Summaries Generated by Large (and Small) Language Models](https://mlsanity.com/paper/2502.20647/) (2025)
- [ResearchPulse: Building Method-Experiment Chains through Multi-Document Scientific Inference](https://mlsanity.com/paper/2509.03565/) (2025)
- [Transductive Learning for Abstractive News Summarization](https://mlsanity.com/paper/2104.09500/) (2021)
- [An entity-guided text summarization framework with relational heterogeneous graph neural network](https://mlsanity.com/paper/2302.03205/) (2023)
- [Enhancing Coherence of Extractive Summarization with Multitask Learning](https://mlsanity.com/paper/2305.12851/) (2023)
- [Unsupervised Extractive Summarization with Learnable Length Control Strategies](https://mlsanity.com/paper/2312.06901/) (2023)
- [Centrality Meets Centroid: A Graph-based Approach for Unsupervised Document Summarization](https://mlsanity.com/paper/2103.15327/) (2021)
- [Augmented Abstractive Summarization With Document-LevelSemantic Graph](https://mlsanity.com/paper/2109.06046/) (2021)
- [Sparse Optimization for Unsupervised Extractive Summarization of Long Documents with the Frank-Wolfe Algorithm](https://mlsanity.com/paper/2208.09454/) (2022)
- [Unsupervised Extractive Summarization with Heterogeneous Graph Embeddings for Chinese Document](https://mlsanity.com/paper/2211.04698/) (2022)

## Abstract (arXiv)

We present a discriminative model for single-document summarization that integrally combines compression and anaphoricity constraints. Our model selects textual units to include in the summary based on a rich set of sparse features whose weights are learned on a large corpus. We allow for the deletion of content within a sentence when that deletion is licensed by compression rules; in our framework, these are implemented as dependencies between subsentential units of text. Anaphoricity constraints then improve cross-sentence coherence by guaranteeing that, for each pronoun included in the summary, the pronoun's antecedent is included as well or the pronoun is rewritten as a full mention. When trained end-to-end, our final system outperforms prior work on both ROUGE as well as on human judgments of linguistic quality.