Improving Keyphrase Extraction with Data Augmentation and Information Filtering

• URL: http://arxiv.org/abs/2209.04951v1
• Date: Sun, 11 Sep 2022 22:38:02 GMT
• Title: Improving Keyphrase Extraction with Data Augmentation and Information Filtering
• Authors: Amir Pouran Ben Veyseh, Nicole Meister, Franck Dernoncourt, Thien Huu Nguyen
• Abstract summary: Keyphrase extraction is one of the essential tasks for document understanding in NLP. We present a novel corpus and method for keyphrase extraction from the videos streamed on the Behance platform.
• Score: 67.43025048639333
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Keyphrase extraction is one of the essential tasks for document understanding in NLP. While the majority of the prior works are dedicated to the formal setting, e.g., books, news or web-blogs, informal texts such as video transcripts are less explored. To address this limitation, in this work we present a novel corpus and method for keyphrase extraction from the transcripts of the videos streamed on the Behance platform. More specifically, in this work, a novel data augmentation is proposed to enrich the model with the background knowledge about the keyphrase extraction task from other domains. Extensive experiments on the proposed dataset dataset show the effectiveness of the introduced method.

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