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" Query Focused Abstractive Summarization Using BERTSUM Model "
Abdullah, Deen Mohammad
Chali, Yllias
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Latin Dissertation
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| Language of Document
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English
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| Record Number
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1054795
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| Doc. No
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TL53912
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| Main Entry
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Abdullah, Deen Mohammad
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| Title & Author
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Query Focused Abstractive Summarization Using BERTSUM Model\ Abdullah, Deen MohammadChali, Yllias
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| College
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University of Lethbridge (Canada)
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| Date
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2020
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| Degree
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M.Sc.
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2020
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| Note
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78 p.
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| Abstract
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In Natural Language Processing, researchers find many challenges on Query Focused Abstractive Summarization (QFAS), where Bidirectional Encoder Representations from Transformers for Summarization (BERTSUM) can be used for both extractive and abstractive summarization. As there is few available datasets for QFAS, we have generated queries for two publicly available datasets, CNN/Daily Mail and Newsroom, according to the context of the documents and summaries. To generate abstractive summaries, we have applied two different approaches, which are Query focused Abstractive and Query focused Extractive then Abstractive summarizations. In the first approach, we have sorted the sentences of the documents from the most query-related sentences to the less query-related sentences, and in the second approach, we have extracted only the query related sentences to fine-tune the BERTSUM model. Our experimental results show that both of our approaches show good results on ROUGE metric for CNN/Daily Mail and Newsroom datasets.
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| Descriptor
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Artificial intelligence
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Computer science
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Engineering
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| Added Entry
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Chali, Yllias
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| Added Entry
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University of Lethbridge (Canada)
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