# Generating Query Focused Summaries from Query-Free Resources

arXiv: https://arxiv.org/abs/2012.14774 · HTML: https://mlsanity.com/paper/2012.14774/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2020.

## Built on by (most cited first, 27 of 27)

- [Exploring Neural Models for Query-Focused Summarization](https://mlsanity.com/paper/2112.07637/) (2021)
- [AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization](https://mlsanity.com/paper/2111.06474/) (2021)
- [LMGQS: A Large-scale Dataset for Query-focused Summarization](https://mlsanity.com/paper/2305.13086/) (2023)
- [QFMTS: Generating Query-Focused Summaries over Multi-Table Inputs](https://mlsanity.com/paper/2405.05109/) (2024)
- [AnnIE: An Annotation Platform for Constructing Complete Open Information Extraction Benchmark](https://mlsanity.com/paper/2109.07464/) (2021)
- [PDSum: Prototype-driven Continuous Summarization of Evolving Multi-document Sets Stream](https://mlsanity.com/paper/2302.05550/) (2023)
- [Few-shot Query-Focused Summarization with Prefix-Merging](https://mlsanity.com/paper/2211.16164/) (2022)
- [Disordered-DABS: A Benchmark for Dynamic Aspect-Based Summarization in Disordered Texts](https://mlsanity.com/paper/2402.10554/) (2024)
- [Tackling Query-Focused Summarization as A Knowledge-Intensive Task: A Pilot Study](https://mlsanity.com/paper/2112.07536/) (2021)
- [SumREN: Summarizing Reported Speech about Events in News](https://mlsanity.com/paper/2212.01146/) (2022)
- [QontSum: On Contrasting Salient Content for Query-focused Summarization](https://mlsanity.com/paper/2307.07586/) (2023)
- [SINC: Self-Supervised In-Context Learning for Vision-Language Tasks](https://mlsanity.com/paper/2307.07742/) (2023)
- [Author-in-the-Loop Response Generation and Evaluation: Integrating Author Expertise and Intent in Responses to Peer Review](https://mlsanity.com/paper/2602.11173/) (2026)
- [milIE: Modular & Iterative Multilingual Open Information Extraction](https://mlsanity.com/paper/2110.08144/) (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)
- [Combining Deep Neural Reranking and Unsupervised Extraction for Multi-Query Focused Summarization](https://mlsanity.com/paper/2302.01148/) (2023)
- [Event-Keyed Summarization](https://mlsanity.com/paper/2402.06973/) (2024)
- [Label-Free Topic-Focused Summarization Using Query Augmentation](https://mlsanity.com/paper/2404.16411/) (2024)
- [Text Summarization with Oracle Expectation](https://mlsanity.com/paper/2209.12714/) (2022)
- [Generating Query Focused Summaries without Fine-tuning the Transformer-based Pre-trained Models](https://mlsanity.com/paper/2303.06230/) (2023)
- [Improving Query-Focused Meeting Summarization with Query-Relevant Knowledge](https://mlsanity.com/paper/2309.02105/) (2023)
- [Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement Learning](https://mlsanity.com/paper/2311.17514/) (2023)
- [Seeing Through the MiRAGE: Evaluating Multimodal Retrieval Augmented Generation](https://mlsanity.com/paper/2510.24870/) (2025)
- [Generative Long-form Question Answering: Relevance, Faithfulness and Succinctness](https://mlsanity.com/paper/2211.08386/) (2022)
- [Does This Summary Answer My Question? Modeling Query-Focused Summary Readers with Rational Speech Acts](https://mlsanity.com/paper/2411.06524/) (2024)
- [Query-Focused Extractive Summarization for Sentiment Explanation](https://mlsanity.com/paper/2509.11989/) (2025)

## Abstract (arXiv)

The availability of large-scale datasets has driven the development of neural models that create generic summaries from single or multiple documents. In this work we consider query focused summarization (QFS), a task for which training data in the form of queries, documents, and summaries is not readily available. We propose to decompose QFS into (1) query modeling (i.e., finding supportive evidence within a set of documents for a query) and (2) conditional language modeling (i.e., summary generation). We introduce MaRGE, a Masked ROUGE Regression framework for evidence estimation and ranking which relies on a unified representation for summaries and queries, so that summaries in generic data can be converted into proxy queries for learning a query model. Experiments across QFS benchmarks and query types show that our model achieves state-of-the-art performance despite learning from weak supervision.