# Query Focused Abstractive Summarization: Incorporating Query Relevance, Multi-Document Coverage, and Summary Length Constraints into seq2seq Models

arXiv: https://arxiv.org/abs/1801.07704 · HTML: https://mlsanity.com/paper/1801.07704/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2018.

## Built on by (most cited first, 30 of 39)

- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization](https://mlsanity.com/paper/2104.05938/) (2021)
- [MS2: Multi-Document Summarization of Medical Studies](https://mlsanity.com/paper/2104.06486/) (2021)
- [OpenCQA: Open-ended Question Answering with Charts](https://mlsanity.com/paper/2210.06628/) (2022)
- [Read before Generate! Faithful Long Form Question Answering with Machine Reading](https://mlsanity.com/paper/2203.00343/) (2022)
- [Exploring Neural Models for Query-Focused Summarization](https://mlsanity.com/paper/2112.07637/) (2021)
- [Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2309.17050/) (2023)
- [Domain Adaptation with Pre-trained Transformers for Query Focused Abstractive Text Summarization](https://mlsanity.com/paper/2112.11670/) (2021)
- [Data Augmentation for Abstractive Query-Focused Multi-Document Summarization](https://mlsanity.com/paper/2103.01863/) (2021)
- [Improve Query Focused Abstractive Summarization by Incorporating Answer Relevance](https://mlsanity.com/paper/2105.12969/) (2021)
- [Controlled Text Reduction](https://mlsanity.com/paper/2210.13449/) (2022)
- [LMGQS: A Large-scale Dataset for Query-focused Summarization](https://mlsanity.com/paper/2305.13086/) (2023)
- [Text Summarization with Latent Queries](https://mlsanity.com/paper/2106.00104/) (2021)
- [iFacetSum: Coreference-based Interactive Faceted Summarization for Multi-Document Exploration](https://mlsanity.com/paper/2109.11621/) (2021)
- [CQSumDP: A ChatGPT-Annotated Resource for Query-Focused Abstractive Summarization Based on Debatepedia](https://mlsanity.com/paper/2305.06147/) (2023)
- [Nutri-bullets: Summarizing Health Studies by Composing Segments](https://mlsanity.com/paper/2103.11921/) (2021)
- [SummerTime: Text Summarization Toolkit for Non-experts](https://mlsanity.com/paper/2108.12738/) (2021)
- [Cross-language Sentence Selection via Data Augmentation and Rationale Training](https://mlsanity.com/paper/2106.02293/) (2021)
- [Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media Posts](https://mlsanity.com/paper/2210.12531/) (2022)
- [Learning to Rank Utterances for Query-Focused Meeting Summarization](https://mlsanity.com/paper/2305.12753/) (2023)
- [QontSum: On Contrasting Salient Content for Query-focused Summarization](https://mlsanity.com/paper/2307.07586/) (2023)
- [FG-RAG: Enhancing Query-Focused Summarization with Context-Aware Fine-Grained Graph RAG](https://mlsanity.com/paper/2504.07103/) (2025)
- [Nutribullets Hybrid: Multi-document Health Summarization](https://mlsanity.com/paper/2104.03465/) (2021)
- [Generating Related Work](https://mlsanity.com/paper/2104.08668/) (2021)
- [Query-Utterance Attention with Joint modeling for Query-Focused Meeting Summarization](https://mlsanity.com/paper/2303.04487/) (2023)
- [Learning to Rank Salient Content for Query-focused Summarization](https://mlsanity.com/paper/2411.00324/) (2024)
- [PREME: Preference-based Meeting Exploration through an Interactive Questionnaire](https://mlsanity.com/paper/2205.02370/) (2022)
- [A Hierarchical Encoding-Decoding Scheme for Abstractive Multi-document Summarization](https://mlsanity.com/paper/2305.08503/) (2023)
- [SummHelper: Collaborative Human-Computer Summarization](https://mlsanity.com/paper/2308.08363/) (2023)
- [Core-based Hierarchies for Efficient GraphRAG](https://mlsanity.com/paper/2603.05207/) (2026)

## Abstract (arXiv)

Query Focused Summarization (QFS) has been addressed mostly using extractive methods. Such methods, however, produce text which suffers from low coherence. We investigate how abstractive methods can be applied to QFS, to overcome such limitations. Recent developments in neural-attention based sequence-to-sequence models have led to state-of-the-art results on the task of abstractive generic single document summarization. Such models are trained in an end to end method on large amounts of training data. We address three aspects to make abstractive summarization applicable to QFS: (a)since there is no training data, we incorporate query relevance into a pre-trained abstractive model; (b) since existing abstractive models are trained in a single-document setting, we design an iterated method to embed abstractive models within the multi-document requirement of QFS; (c) the abstractive models we adapt are trained to generate text of specific length (about 100 words), while we aim at generating output of a different size (about 250 words); we design a way to adapt the target size of the generated summaries to a given size ratio. We compare our method (Relevance Sensitive Attention for QFS) to extractive baselines and with various ways to combine abstractive models on the DUC QFS datasets and demonstrate solid improvements on ROUGE performance.