Beyond Relevant Documents: A Knowledge-Intensive Approach for Query-Focused Summarization using Large Language Models
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913473192001536 |
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| author | Zhang, Weijia Huang, Jia-Hong Vakulenko, Svitlana Xu, Yumo Rajapakse, Thilina Kanoulas, Evangelos |
| author_facet | Zhang, Weijia Huang, Jia-Hong Vakulenko, Svitlana Xu, Yumo Rajapakse, Thilina Kanoulas, Evangelos |
| contents | Query-focused summarization (QFS) is a fundamental task in natural language processing with broad applications, including search engines and report generation. However, traditional approaches assume the availability of relevant documents, which may not always hold in practical scenarios, especially in highly specialized topics. To address this limitation, we propose a novel knowledge-intensive approach that reframes QFS as a knowledge-intensive task setup. This approach comprises two main components: a retrieval module and a summarization controller. The retrieval module efficiently retrieves potentially relevant documents from a large-scale knowledge corpus based on the given textual query, eliminating the dependence on pre-existing document sets. The summarization controller seamlessly integrates a powerful large language model (LLM)-based summarizer with a carefully tailored prompt, ensuring the generated summary is comprehensive and relevant to the query. To assess the effectiveness of our approach, we create a new dataset, along with human-annotated relevance labels, to facilitate comprehensive evaluation covering both retrieval and summarization performance. Extensive experiments demonstrate the superior performance of our approach, particularly its ability to generate accurate summaries without relying on the availability of relevant documents initially. This underscores our method's versatility and practical applicability across diverse query scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_10357 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Beyond Relevant Documents: A Knowledge-Intensive Approach for Query-Focused Summarization using Large Language Models Zhang, Weijia Huang, Jia-Hong Vakulenko, Svitlana Xu, Yumo Rajapakse, Thilina Kanoulas, Evangelos Computation and Language Information Retrieval Query-focused summarization (QFS) is a fundamental task in natural language processing with broad applications, including search engines and report generation. However, traditional approaches assume the availability of relevant documents, which may not always hold in practical scenarios, especially in highly specialized topics. To address this limitation, we propose a novel knowledge-intensive approach that reframes QFS as a knowledge-intensive task setup. This approach comprises two main components: a retrieval module and a summarization controller. The retrieval module efficiently retrieves potentially relevant documents from a large-scale knowledge corpus based on the given textual query, eliminating the dependence on pre-existing document sets. The summarization controller seamlessly integrates a powerful large language model (LLM)-based summarizer with a carefully tailored prompt, ensuring the generated summary is comprehensive and relevant to the query. To assess the effectiveness of our approach, we create a new dataset, along with human-annotated relevance labels, to facilitate comprehensive evaluation covering both retrieval and summarization performance. Extensive experiments demonstrate the superior performance of our approach, particularly its ability to generate accurate summaries without relying on the availability of relevant documents initially. This underscores our method's versatility and practical applicability across diverse query scenarios. |
| title | Beyond Relevant Documents: A Knowledge-Intensive Approach for Query-Focused Summarization using Large Language Models |
| topic | Computation and Language Information Retrieval |
| url | https://arxiv.org/abs/2408.10357 |