# AttSum: Joint Learning of Focusing and Summarization with Neural Attention

arXiv: https://arxiv.org/abs/1604.00125 · HTML: https://mlsanity.com/paper/1604.00125/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2016.

## Built on by (most cited first, 7 of 7)

- [A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models](https://mlsanity.com/paper/2406.11289/) (2024)
- [A comprehensive review of automatic text summarization techniques: method, data, evaluation and coding](https://mlsanity.com/paper/2301.03403/) (2023)
- [From News to Summaries: Building a Hungarian Corpus for Extractive and Abstractive Summarization](https://mlsanity.com/paper/2404.03555/) (2024)
- [Towards Reliable and Factual Response Generation: Detecting Unanswerable Questions in Information-Seeking Conversations](https://mlsanity.com/paper/2401.11452/) (2024)
- [Improving Online Forums Summarization via Hierarchical Unified Deep Neural Network](https://mlsanity.com/paper/2103.13587/) (2021)
- [Using Query Expansion in Manifold Ranking for Query-Oriented Multi-Document Summarization](https://mlsanity.com/paper/2108.01441/) (2021)
- [MetaSynth: Multi-Agent Metadata Generation from Implicit Feedback in Black-Box Systems](https://mlsanity.com/paper/2510.01523/) (2025)

## Abstract (arXiv)

Query relevance ranking and sentence saliency ranking are the two main tasks in extractive query-focused summarization. Previous supervised summarization systems often perform the two tasks in isolation. However, since reference summaries are the trade-off between relevance and saliency, using them as supervision, neither of the two rankers could be trained well. This paper proposes a novel summarization system called AttSum, which tackles the two tasks jointly. It automatically learns distributed representations for sentences as well as the document cluster. Meanwhile, it applies the attention mechanism to simulate the attentive reading of human behavior when a query is given. Extensive experiments are conducted on DUC query-focused summarization benchmark datasets. Without using any hand-crafted features, AttSum achieves competitive performance. It is also observed that the sentences recognized to focus on the query indeed meet the query need.