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DOČEKAL, M.; SMRŽ, P.
Original Title
Query-Based Keyphrase Extraction from Long Documents
English Title
Type
Paper in proceedings (conference paper)
Original Abstract
Transformer-based architectures in natural language processing force input size limits that can be problematic when long documents need to be processed. This paper overcomes this issue for keyphrase extraction by chunking the long documents while keeping a global context as a query defining the topic for which relevant keyphrases should be extracted. The developed system employs a pre-trained BERT model and adapts it to estimate the probability that a given text span forms a keyphrase. We experimented using various context sizes on two popular datasets, Inspec and SemEval, and a large novel dataset. The presented results show that a shorter context with a query overcomes a longer one without the query on long documents.
English abstract
Keywords
keyphrase,keyword,long documents,query-based keyphrase extraction,BERT,transformer
Key words in English
Authors
RIV year
2023
Released
04.05.2022
Publisher
LibraryPress@UF
Location
Jensen Beach
Book
The International FLAIRS Conference Proceedings
Edition
2022
ISBN
2334-0762
Periodical
Proceedings of the ... International Florida Artificial Intelligence Research Society Conference
Volume
Number
35
State
United States of America
Pages from
1
Pages to
4
Pages count
URL
https://journals.flvc.org/FLAIRS/article/view/130737
BibTex
@inproceedings{BUT179282, author="Martin {Dočekal} and Pavel {Smrž}", title="Query-Based Keyphrase Extraction from Long Documents", booktitle="The International FLAIRS Conference Proceedings", year="2022", series="2022", journal="Proceedings of the ... International Florida Artificial Intelligence Research Society Conference", volume="2022", number="35", pages="1--4", publisher="LibraryPress@UF", address="Jensen Beach", doi="10.32473/flairs.v35i.130737", issn="2334-0754", url="https://journals.flvc.org/FLAIRS/article/view/130737" }
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