# Syntactically Look-Ahead Attention Network for Sentence Compression

arXiv: https://arxiv.org/abs/2002.01145 · HTML: https://mlsanity.com/paper/2002.01145/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2020.

## Built on by (most cited first, 6 of 6)

- [Revision for Concision: A Constrained Paraphrase Generation Task](https://mlsanity.com/paper/2210.14257/) (2022)
- [InstructCMP: Length Control in Sentence Compression through Instruction-based Large Language Models](https://mlsanity.com/paper/2406.11097/) (2024)
- [Contextualized Semantic Distance between Highly Overlapped Texts](https://mlsanity.com/paper/2110.01176/) (2021)
- [Considering Length Diversity in Retrieval-Augmented Summarization](https://mlsanity.com/paper/2503.09249/) (2025)
- [Evaluation Discrepancy Discovery: A Sentence Compression Case-study](https://mlsanity.com/paper/2101.09079/) (2021)
- [From Lengthy to Lucid: A Systematic Literature Review on NLP Techniques for Taming Long Sentences](https://mlsanity.com/paper/2312.05172/) (2023)

## Abstract (arXiv)

Sentence compression is the task of compressing a long sentence into a short one by deleting redundant words. In sequence-to-sequence (Seq2Seq) based models, the decoder unidirectionally decides to retain or delete words. Thus, it cannot usually explicitly capture the relationships between decoded words and unseen words that will be decoded in the future time steps. Therefore, to avoid generating ungrammatical sentences, the decoder sometimes drops important words in compressing sentences. To solve this problem, we propose a novel Seq2Seq model, syntactically look-ahead attention network (SLAHAN), that can generate informative summaries by explicitly tracking both dependency parent and child words during decoding and capturing important words that will be decoded in the future. The results of the automatic evaluation on the Google sentence compression dataset showed that SLAHAN achieved the best kept-token-based-F1, ROUGE-1, ROUGE-2 and ROUGE-L scores of 85.5, 79.3, 71.3 and 79.1, respectively. SLAHAN also improved the summarization performance on longer sentences. Furthermore, in the human evaluation, SLAHAN improved informativeness without losing readability.