# Neural Extractive Text Summarization with Syntactic Compression

arXiv: https://arxiv.org/abs/1902.00863 · HTML: https://mlsanity.com/paper/1902.00863/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2019.

## Built on by (most cited first, 30 of 37)

- [QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization](https://mlsanity.com/paper/2104.05938/) (2021)
- [Robustness Gym: Unifying the NLP Evaluation Landscape](https://mlsanity.com/paper/2101.04840/) (2021)
- [Tractable Control for Autoregressive Language Generation](https://mlsanity.com/paper/2304.07438/) (2023)
- [DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization](https://mlsanity.com/paper/2110.08168/) (2021)
- [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)
- [BASS: Boosting Abstractive Summarization with Unified Semantic Graph](https://mlsanity.com/paper/2105.12041/) (2021)
- [Multiplex Graph Neural Network for Extractive Text Summarization](https://mlsanity.com/paper/2108.12870/) (2021)
- [Automatic Text Summarization Methods: A Comprehensive Review](https://mlsanity.com/paper/2204.01849/) (2022)
- [Incorporating Distributions of Discourse Structure for Long Document Abstractive Summarization](https://mlsanity.com/paper/2305.16784/) (2023)
- [NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist](https://mlsanity.com/paper/2305.08566/) (2023)
- [StreamHover: Livestream Transcript Summarization and Annotation](https://mlsanity.com/paper/2109.05160/) (2021)
- [A Character-Level Length-Control Algorithm for Non-Autoregressive Sentence Summarization](https://mlsanity.com/paper/2205.14522/) (2022)
- [Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution](https://mlsanity.com/paper/2106.01518/) (2021)
- [Attention Head Masking for Inference Time Content Selection in Abstractive Summarization](https://mlsanity.com/paper/2104.02205/) (2021)
- [Leveraging Locality in Abstractive Text Summarization](https://mlsanity.com/paper/2205.12476/) (2022)
- [Massive-scale Decoding for Text Generation using Lattices](https://mlsanity.com/paper/2112.07660/) (2021)
- [SummerTime: Text Summarization Toolkit for Non-experts](https://mlsanity.com/paper/2108.12738/) (2021)
- [Human Guided Exploitation of Interpretable Attention Patterns in Summarization and Topic Segmentation](https://mlsanity.com/paper/2112.05364/) (2021)
- [Investigating Non-local Features for Neural Constituency Parsing](https://mlsanity.com/paper/2109.12814/) (2021)
- [An Efficient Coarse-to-Fine Facet-Aware Unsupervised Summarization Framework based on Semantic Blocks](https://mlsanity.com/paper/2208.08253/) (2022)
- [EDU-level Extractive Summarization with Varying Summary Lengths](https://mlsanity.com/paper/2210.04029/) (2022)
- [GENUINE: Graph Enhanced Multi-level Uncertainty Estimation for Large Language Models](https://mlsanity.com/paper/2509.07925/) (2025)
- [Extractive Summarization of Call Transcripts](https://mlsanity.com/paper/2103.10599/) (2021)
- [Contextualized Semantic Distance between Highly Overlapped Texts](https://mlsanity.com/paper/2110.01176/) (2021)
- [Constituency Parsing using LLMs](https://mlsanity.com/paper/2310.19462/) (2023)
- [Transductive Learning for Abstractive News Summarization](https://mlsanity.com/paper/2104.09500/) (2021)
- [A Condense-then-Select Strategy for Text Summarization](https://mlsanity.com/paper/2106.10468/) (2021)
- [An Overview of Indian Language Datasets used for Text Summarization](https://mlsanity.com/paper/2203.16127/) (2022)
- [Implementing Deep Learning-Based Approaches for Article Summarization in Indian Languages](https://mlsanity.com/paper/2212.05702/) (2022)

## Abstract (arXiv)

Recent neural network approaches to summarization are largely either selection-based extraction or generation-based abstraction. In this work, we present a neural model for single-document summarization based on joint extraction and syntactic compression. Our model chooses sentences from the document, identifies possible compressions based on constituency parses, and scores those compressions with a neural model to produce the final summary. For learning, we construct oracle extractive-compressive summaries, then learn both of our components jointly with this supervision. Experimental results on the CNN/Daily Mail and New York Times datasets show that our model achieves strong performance (comparable to state-of-the-art systems) as evaluated by ROUGE. Moreover, our approach outperforms an off-the-shelf compression module, and human and manual evaluation shows that our model's output generally remains grammatical.