# BiSET: Bi-directional Selective Encoding with Template for Abstractive Summarization

arXiv: https://arxiv.org/abs/1906.05012 · HTML: https://mlsanity.com/paper/1906.05012/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2019.

## Built on by (most cited first, 8 of 8)

- [How well do you know your summarization datasets?](https://mlsanity.com/paper/2106.11388/) (2021)
- [A New Approach to Overgenerating and Scoring Abstractive Summaries](https://mlsanity.com/paper/2104.01726/) (2021)
- [Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Order](https://mlsanity.com/paper/2301.00867/) (2023)
- [Automatic Document Sketching: Generating Drafts from Analogous Texts](https://mlsanity.com/paper/2106.07192/) (2021)
- [TacoERE: Cluster-aware Compression for Event Relation Extraction](https://mlsanity.com/paper/2405.06890/) (2024)
- [There Once Was a Really Bad Poet, It Was Automated but You Didn't Know It](https://mlsanity.com/paper/2103.03775/) (2021)
- [Learning Summary-Worthy Visual Representation for Abstractive Summarization in Video](https://mlsanity.com/paper/2305.04824/) (2023)
- [Write Summary Step-by-Step: A Pilot Study of Stepwise Summarization](https://mlsanity.com/paper/2406.05361/) (2024)

## Abstract (arXiv)

The success of neural summarization models stems from the meticulous encodings of source articles. To overcome the impediments of limited and sometimes noisy training data, one promising direction is to make better use of the available training data by applying filters during summarization. In this paper, we propose a novel Bi-directional Selective Encoding with Template (BiSET) model, which leverages template discovered from training data to softly select key information from each source article to guide its summarization process. Extensive experiments on a standard summarization dataset were conducted and the results show that the template-equipped BiSET model manages to improve the summarization performance significantly with a new state of the art.