# Improve Query Focused Abstractive Summarization by Incorporating Answer Relevance

arXiv: https://arxiv.org/abs/2105.12969 · HTML: https://mlsanity.com/paper/2105.12969/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 49 references that are in the map.
- First posted on arXiv 27 May 2021.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [A Deep Reinforced Model for Abstractive Summarization](https://mlsanity.com/paper/1705.04304/) (2017)
- [MASS: Masked Sequence to Sequence Pre-training for Language Generation](https://mlsanity.com/paper/1905.02450/) (2019)
- [NewsQA: A Machine Comprehension Dataset](https://mlsanity.com/paper/1611.09830/) (2016)
- [Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies](https://mlsanity.com/paper/1804.11283/) (2018)
- [SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine](https://mlsanity.com/paper/1704.05179/) (2017)
- [Bottom-Up Abstractive Summarization](https://mlsanity.com/paper/1808.10792/) (2018)
- [End-to-End Open-Domain Question Answering with BERTserini](https://mlsanity.com/paper/1902.01718/) (2019)
- [CrossNER: Evaluating Cross-Domain Named Entity Recognition](https://mlsanity.com/paper/2012.04373/) (2020)
- [AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization](https://mlsanity.com/paper/2103.11332/) (2021)
- [Diversity driven Attention Model for Query-based Abstractive Summarization](https://mlsanity.com/paper/1704.08300/) (2017)
- [ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation](https://mlsanity.com/paper/2001.11314/) (2020)
- [Query Focused Abstractive Summarization: Incorporating Query Relevance, Multi-Document Coverage, and Summary Length Constraints into seq2seq Models](https://mlsanity.com/paper/1801.07704/) (2018)
- [A Pilot Study of Domain Adaptation Effect for Neural Abstractive Summarization](https://mlsanity.com/paper/1707.07062/) (2017)
- [Conditional Self-Attention for Query-based Summarization](https://mlsanity.com/paper/2002.07338/) (2020)
- [Query Focused Multi-Document Summarization with Distant Supervision](https://mlsanity.com/paper/2004.03027/) (2020)
- [CAiRE-COVID: A Question Answering and Query-focused Multi-Document Summarization System for COVID-19 Scholarly Information Management](https://mlsanity.com/paper/2005.03975/) (2020)

## Built on by (most cited first, 15 of 15)

- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [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)
- [Domain Adaptation with Pre-trained Transformers for Query Focused Abstractive Text Summarization](https://mlsanity.com/paper/2112.11670/) (2021)
- [AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization](https://mlsanity.com/paper/2111.06474/) (2021)
- [A Lightweight Constrained Generation Alternative for Query-focused Summarization](https://mlsanity.com/paper/2304.11721/) (2023)
- [Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media Posts](https://mlsanity.com/paper/2210.12531/) (2022)
- [Few-shot Query-Focused Summarization with Prefix-Merging](https://mlsanity.com/paper/2211.16164/) (2022)
- [QontSum: On Contrasting Salient Content for Query-focused Summarization](https://mlsanity.com/paper/2307.07586/) (2023)
- [Learning to Rank Salient Content for Query-focused Summarization](https://mlsanity.com/paper/2411.00324/) (2024)
- [Instructive Dialogue Summarization with Query Aggregations](https://mlsanity.com/paper/2310.10981/) (2023)
- [Label-Free Topic-Focused Summarization Using Query Augmentation](https://mlsanity.com/paper/2404.16411/) (2024)
- [Generating Query Focused Summaries without Fine-tuning the Transformer-based Pre-trained Models](https://mlsanity.com/paper/2303.06230/) (2023)
- [Does This Summary Answer My Question? Modeling Query-Focused Summary Readers with Rational Speech Acts](https://mlsanity.com/paper/2411.06524/) (2024)
- [Generating Query-Focused Summarization Datasets from Query-Free Summarization Datasets](https://mlsanity.com/paper/2605.05392/) (2026)

## Abstract (arXiv)

Query focused summarization (QFS) models aim to generate summaries from source documents that can answer the given query. Most previous work on QFS only considers the query relevance criterion when producing the summary. However, studying the effect of answer relevance in the summary generating process is also important. In this paper, we propose QFS-BART, a model that incorporates the explicit answer relevance of the source documents given the query via a question answering model, to generate coherent and answer-related summaries. Furthermore, our model can take advantage of large pre-trained models which improve the summarization performance significantly. Empirical results on the Debatepedia dataset show that the proposed model achieves the new state-of-the-art performance.