# Selective Encoding for Abstractive Sentence Summarization

arXiv: https://arxiv.org/abs/1704.07073 · HTML: https://mlsanity.com/paper/1704.07073/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2017.

## Built on by (most cited first, 17 of 17)

- [Focus Attention: Promoting Faithfulness and Diversity in Summarization](https://mlsanity.com/paper/2105.11921/) (2021)
- [A New Approach to Overgenerating and Scoring Abstractive Summaries](https://mlsanity.com/paper/2104.01726/) (2021)
- [Attention Head Masking for Inference Time Content Selection in Abstractive Summarization](https://mlsanity.com/paper/2104.02205/) (2021)
- [XL-HeadTags: Leveraging Multimodal Retrieval Augmentation for the Multilingual Generation of News Headlines and Tags](https://mlsanity.com/paper/2406.03776/) (2024)
- [PROM: A Phrase-level Copying Mechanism with Pre-training for Abstractive Summarization](https://mlsanity.com/paper/2305.06647/) (2023)
- [Benchmarking LLMs on the Semantic Overlap Summarization Task](https://mlsanity.com/paper/2402.17008/) (2024)
- [Attend and select: A segment selective transformer for microblog hashtag generation](https://mlsanity.com/paper/2106.03151/) (2021)
- [Neural Abstractive Unsupervised Summarization of Online News Discussions](https://mlsanity.com/paper/2106.03953/) (2021)
- [CUSTOM: Aspect-Oriented Product Summarization for E-Commerce](https://mlsanity.com/paper/2108.08010/) (2021)
- [KATSum: Knowledge-aware Abstractive Text Summarization](https://mlsanity.com/paper/2212.03371/) (2022)
- [CSTRL: Context-Driven Sequential Transfer Learning for Abstractive Radiology Report Summarization](https://mlsanity.com/paper/2503.05750/) (2025)
- [Alleviate Exposure Bias in Sequence Prediction  with Recurrent Neural Networks](https://mlsanity.com/paper/2103.11603/) (2021)
- [Enhanced Seq2Seq Autoencoder via Contrastive Learning for Abstractive Text Summarization](https://mlsanity.com/paper/2108.11992/) (2021)
- [Multi-Narrative Semantic Overlap Task: Evaluation and Benchmark](https://mlsanity.com/paper/2201.05294/) (2022)
- [CFSum: A Coarse-to-Fine Contribution Network for Multimodal Summarization](https://mlsanity.com/paper/2307.02716/) (2023)
- [From Lengthy to Lucid: A Systematic Literature Review on NLP Techniques for Taming Long Sentences](https://mlsanity.com/paper/2312.05172/) (2023)
- [BLooP: Zero-Shot Abstractive Summarization using Large Language Models with Bigram Lookahead Promotion](https://mlsanity.com/paper/2603.11415/) (2026)

## Abstract (arXiv)

We propose a selective encoding model to extend the sequence-to-sequence framework for abstractive sentence summarization. It consists of a sentence encoder, a selective gate network, and an attention equipped decoder. The sentence encoder and decoder are built with recurrent neural networks. The selective gate network constructs a second level sentence representation by controlling the information flow from encoder to decoder. The second level representation is tailored for sentence summarization task, which leads to better performance. We evaluate our model on the English Gigaword, DUC 2004 and MSR abstractive sentence summarization datasets. The experimental results show that the proposed selective encoding model outperforms the state-of-the-art baseline models.