# Query Focused Multi-Document Summarization with Distant Supervision

arXiv: https://arxiv.org/abs/2004.03027 · HTML: https://mlsanity.com/paper/2004.03027/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2020.

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

- [How Do We Answer Complex Questions: Discourse Structure of Long-form Answers](https://mlsanity.com/paper/2203.11048/) (2022)
- [Improve Query Focused Abstractive Summarization by Incorporating Answer Relevance](https://mlsanity.com/paper/2105.12969/) (2021)
- [Concise Answers to Complex Questions: Summarization of Long-form Answers](https://mlsanity.com/paper/2305.19271/) (2023)
- [Hone as You Read: A Practical Type of Interactive Summarization](https://mlsanity.com/paper/2105.02923/) (2021)
- [On The Persona-based Summarization of Domain-Specific Documents](https://mlsanity.com/paper/2406.03986/) (2024)
- [Generative Long-form Question Answering: Relevance, Faithfulness and Succinctness](https://mlsanity.com/paper/2211.08386/) (2022)

## Abstract (arXiv)

We consider the problem of better modeling query-cluster interactions to facilitate query focused multi-document summarization (QFS). Due to the lack of training data, existing work relies heavily on retrieval-style methods for estimating the relevance between queries and text segments. In this work, we leverage distant supervision from question answering where various resources are available to more explicitly capture the relationship between queries and documents. We propose a coarse-to-fine modeling framework which introduces separate modules for estimating whether segments are relevant to the query, likely to contain an answer, and central. Under this framework, a trained evidence estimator further discerns which retrieved segments might answer the query for final selection in the summary. We demonstrate that our framework outperforms strong comparison systems on standard QFS benchmarks.