# Query-Based Abstractive Summarization Using Neural Networks

arXiv: https://arxiv.org/abs/1712.06100 · HTML: https://mlsanity.com/paper/1712.06100/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2017.

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

- [Few-shot Query-Focused Summarization with Prefix-Merging](https://mlsanity.com/paper/2211.16164/) (2022)
- [Hone as You Read: A Practical Type of Interactive Summarization](https://mlsanity.com/paper/2105.02923/) (2021)
- [Saved You A Click: Automatically Answering Clickbait Titles](https://mlsanity.com/paper/2212.08196/) (2022)
- [Surveying the Landscape of Text Summarization with Deep Learning: A Comprehensive Review](https://mlsanity.com/paper/2310.09411/) (2023)
- [How to Train Text Summarization Model with Weak Supervisions](https://mlsanity.com/paper/2409.00098/) (2024)
- [Generating Query-Focused Summarization Datasets from Query-Free Summarization Datasets](https://mlsanity.com/paper/2605.05392/) (2026)

## Abstract (arXiv)

In this paper, we present a model for generating summaries of text documents with respect to a query. This is known as query-based summarization. We adapt an existing dataset of news article summaries for the task and train a pointer-generator model using this dataset. The generated summaries are evaluated by measuring similarity to reference summaries. Our results show that a neural network summarization model, similar to existing neural network models for abstractive summarization, can be constructed to make use of queries to produce targeted summaries.