Publication Details
Query-Based Keyphrase Extraction from Long Documents
keyphrase,keyword,long documents,query-based keyphrase
extraction,BERT,transformer
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.
@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",
volume="2022",
number="35",
pages="1--4",
publisher="LibraryPress@UF",
address="Jensen Beach",
doi="10.32473/flairs.v35i.130737",
issn="2334-0762",
url="https://journals.flvc.org/FLAIRS/article/view/130737"
}