What is it about?
Our proposed work aims to enhance the quality of abstractive text summaries by extracting the most significant sentences from a document rather than the entire document to generate a summary. Current summarization models tend to produce subpar summaries, and this work looks to tackle this problem by concentrating on the essential sentences of the document.
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Perspectives
The performance of abstractive text summarization models depends on the model's ability to generate quality summaries by selecting the most pertinent sentences from the document. The proposed "Content Selector'' module has been shown to improve the quality of the generated summaries by providing the summarization models with the most important sentences. This work highlights the necessity for a new evaluation metric for this area.
Dr. Sanjay Singh
Manipal Institute of Technology, Manipal
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This page is a summary of: Quality Enhancement of Abstractive Text Summaries with Content Selection, November 2022, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/cict56698.2022.9997835.
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