Improving Keyphrase Extraction with Data Augmentation and Information Filtering
Computation and Language
2022-09-13 v1
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.
Cite
@article{arxiv.2209.04951,
title = {Improving Keyphrase Extraction with Data Augmentation and Information Filtering},
author = {Amir Pouran Ben Veyseh and Nicole Meister and Franck Dernoncourt and Thien Huu Nguyen},
journal= {arXiv preprint arXiv:2209.04951},
year = {2022}
}