Query-Based Keyphrase Extraction from Long Documents

• URL: http://arxiv.org/abs/2205.05391v1
• Date: Wed, 11 May 2022 10:29:30 GMT
• Title: Query-Based Keyphrase Extraction from Long Documents
• Authors: Martin Docekal, Pavel Smrz
• Abstract summary: This paper overcomes issue for keyphrase extraction by chunking the long documents. System employs a pre-trained BERT model and adapts it to estimate the probability that a given text span forms a keyphrase.
• Score: 4.823229052465654
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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