| Challenge: | Existing datasets are small for data-hungry neural architectures and are limited to evaluation purposes. |
| Approach: | They propose to decompose QFS into query modeling and conditional language modeling . they propose a Masked ROUGE Regression framework for evidence estimation and ranking . |
| Outcome: | The proposed model achieves state-of-the-art performance despite weak supervision. |
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| Challenge: | Existing benchmarks for query-focused summarization are small for training large neural models. |
| Approach: | They propose a unified modeling framework for query-focused summarization . they model queries as discrete latent variables over document tokens . |
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LMGQS: A Large-scale Dataset for Query-focused Summarization (2023.findings-emnlp)
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Ruochen Xu, Song Wang, Yang Liu, Shuohang Wang, Yichong Xu, Dan Iter, Pengcheng He, Chenguang Zhu, Michael Zeng
| Challenge: | Lack of large-scale datasets for query-focused summarization hinders model development . lack of data limits the ability of QFS models to train robust neural models . |
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ROUGE-SciQFS: A ROUGE-based Method to Automatically Create Datasets for Scientific Query-Focused Summarization (2025.coling-main)
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| Challenge: | Scientific Query-Focused Summarization (Sci-QFS) has lagged in development due to the lack of data. |
| Approach: | They propose a method to take advantage of existing academic papers to obtain large-scale datasets for this task automatically. |
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Diffusion Language Model with Query-Document Relevance for Query-Focused Summarization (2023.findings-emnlp)
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| Challenge: | Query-Focused Summarization (QFS) aims to generate summaries that address specific queries by extracting crucial information from source documents. |
| Approach: | They propose a non-autoregressive diffusion language model that incorporates query-document fragment relevance and query-doctoral global relevance to enhance the adaptability of QFS tasks. |
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Exploring Neural Models for Query-Focused Summarization (2022.findings-naacl)
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| Challenge: | Recent work in Query-focused summarization lacks a comprehensive study of the broad space of applicable modeling methods. |
| Approach: | They propose to explore two general classes of methods for Query-focused summarization: extractive-abstractive solutions and end-to-end models. |
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Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement Learning (2023.emnlp-main)
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| Challenge: | Query-focused Summarization (QfS) is a system that generates summaries from document(s) based on a query. |
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Improve Query Focused Abstractive Summarization by Incorporating Answer Relevance (2021.findings-acl)
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| Challenge: | Query focused summarization models aim to generate summaries from source documents that can answer the given query. |
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Learning to Rank Salient Content for Query-focused Summarization (2024.emnlp-main)
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| Challenge: | Query-focused summarization (QFS) is gaining prominence in research community. |
| Approach: | They propose to integrate Learning-to-Rank (LTR) with Query-focused Summarization (QFS) to enhance the summary relevance via content prioritization. |
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WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization (2020.coling-main)
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| Challenge: | Existing methods to generate abstractive summarizations are lacking labeled training datasets. |
| Approach: | They propose a weakly supervised approach to generate a strong summary from a set of documents based on a query. |
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MoDS: Moderating a Mixture of Document Speakers to Summarize Debatable Queries in Document Collections (2025.naacl-long)
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Nishant Balepur, Alexa Siu, Nedim Lipka, Franck Dernoncourt, Tong Sun, Jordan Lee Boyd-Graber, Puneet Mathur
| Challenge: | Query-focused summarization (QFS) gives an overview of documents to answer a query, ignoring debatable ones. |
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