mlsanityData to 7 Oct 2026

CQSumDP: A ChatGPT-Annotated Resource for Query-Focused Abstractive Summarization Based on Debatepedia

arXiv 2305.06147 ↗First posted 31 Mar 2023cs.CLcited by 7

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Abstract (arXiv)

Debatepedia is a publicly available dataset consisting of arguments and counter-arguments on controversial topics that has been widely used for the single-document query-focused abstractive summarization task in recent years. However, it has been recently found that this dataset is limited by noise and even most queries in this dataset do not have any relevance to the respective document. In this paper, we present a methodology for cleaning the Debatepedia dataset by leveraging the generative power of large language models to make it suitable for query-focused abstractive summarization. More specifically, we harness the language generation capabilities of ChatGPT to regenerate its queries. We evaluate the effectiveness of the proposed ChatGPT annotated version of the Debatepedia dataset using several benchmark summarization models and demonstrate that the newly annotated version of Debatepedia outperforms the original dataset in terms of both query relevance as well as summary generation quality. We will make this annotated and cleaned version of the dataset publicly available.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 43 of its 62 references that are in the map. First posted on arXiv 31 Mar 2023.

Builds on

43 of 62 references.

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Cited by year

Citing papers per year: 2023: 3; 2024: 2; 2025: 2.

7 papers.

  1. 2023A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasetscited by 123
  2. 2024A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendationscited by 84
  3. 2023Retrieve-Rewrite-Answer: A KG-to-Text Enhanced LLMs Framework for Knowledge Graph Question Answeringcited by 64
  4. 2023LeanContext: Cost-Efficient Domain-Specific Question Answering Using LLMscited by 40
  5. 2024QFMTS: Generating Query-Focused Summaries over Multi-Table Inputscited by 9
  6. 2025Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applicationscited by 4
  7. 2025MSRS: Evaluating Multi-Source Retrieval-Augmented Generationcited by 0