CQSumDP: a ChatGPT-annotated resource for query-focused abstractive summarization based on debatepedia
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 …
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 …
CQSumDP: A ChatGPT-Annotated Resource for Query-Focused Abstractive Summarization Based on Debatepedia
M Tahmid Rahman Laskar, M Rahman… - arXiv e …, 2023 - ui.adsabs.harvard.edu
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 …
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 …
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