https://github.com/salesforce/query-focused-sum

Official code repository for "Exploring Neural Models for Query-Focused Summarization".
https://github.com/salesforce/query-focused-sum

deep-learning machine-learning neural-network nlp question-answering summarization

Last synced: 7 days ago
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Official code repository for "Exploring Neural Models for Query-Focused Summarization".

• Host: GitHub
• URL: https://github.com/salesforce/query-focused-sum
• Owner: salesforce
• Created: 2021-12-09T23:53:46.000Z (almost 3 years ago)
• Default Branch: master
• Last Pushed: 2023-06-12T21:32:58.000Z (over 1 year ago)
• Last Synced: 2024-04-08T00:13:13.960Z (7 months ago)
• Topics: deep-learning, machine-learning, neural-network, nlp, question-answering, summarization
• Language: Python
• Homepage: https://arxiv.org/abs/2112.07637
• Size: 43.9 KB
• Stars: 42
• Watchers: 8
• Forks: 5
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt
  • Code of conduct: CODE_OF_CONDUCT.md
  • Codeowners: CODEOWNERS
  • Security: SECURITY.md

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README

        
# Exploring Neural Models for Query-Focused Summarization

This is the official code repository for [Exploring Neural Models for Query-Focused Summarization](https://arxiv.org/abs/2112.07637)

by [Jesse Vig*](https://twitter.com/jesse_vig), [Alexander R. Fabbri*](https://twitter.com/alexfabbri4),

[Wojciech Kryściński*](https://twitter.com/iam_wkr), [Chien-Sheng Wu](https://twitter.com/jasonwu0731), and

[Wenhao Liu](https://twitter.com/owenhaoliu) (*equal contribution). 

We present code and instructions for reproducing the paper experiments and running the models against your own datasets.

## Table of contents

- [Introduction](#introduction)

- [Two-stage models](#two-stage-models)

- [Segment Encoder](#segment-encoder)

- [Citation](#citation)

- [License](#license)

## Introduction

Query-focused summarization (QFS) aims to produce summaries that answer particular questions of interest, enabling greater user control and personalization.

In [our paper](https://arxiv.org/abs/2112.07637) we conduct a systematic exploration of neural approaches to QFS, considering two general classes of methods: two-stage extractive-abstractive solutions and end-to-end models.

Within those categories, we investigate existing methods and present two model extensions that achieve state-of-the-art performance on the QMSum dataset  by a margin of up to 3.38 ROUGE-1, 3.72 ROUGE-2, and 3.28 ROUGE-L.

## Two-stage models

Two-step approaches consist of an *extractor* model, which extracts parts of the source document relevant to the input query, and an *abstractor* model,

which synthesizes the extracted segments into a final summary.

See [extractors](extractors/README.md) directory for instructions and code for training and evaluating two-stage models.

## Segment Encoder

The Segment Encoder is an end-to-end model that uses sparse local attention to achieve SOTA ROUGE scores on the QMSum dataset.

To [replicate](multiencoder/README.md#reproducing-qmsum-experiments) the QMSum experiments, or train and evaluate Segment Encoder

[on your own dataset](multiencoder/README.md#running-on-your-own-datasets), see the 

 [multiencoder](multiencoder/README.md) directory.

## Citation

When referencing this repository, please cite [this paper](https://arxiv.org/abs/2112.07637):

```bibtex

@misc{vig-etal-2021-exploring,

      title={Exploring Neural Models for Query-Focused Summarization}, 

      author={Jesse Vig and Alexander R. Fabbri and Wojciech Kry{\'s}ci{\'n}ski and Chien-Sheng Wu and Wenhao Liu},

      year={2021},

      eprint={2112.07637},

      archivePrefix={arXiv},

      primaryClass={cs.CL},

      url={https://arxiv.org/abs/2112.07637}

}

```

## License

This repository is released under the [BSD-3 License](LICENSE.txt).