Open Domain Web Keyphrase Extraction Beyond Language Modeling
Abstract
This paper studies keyphrase extraction in real-world scenarios where documents are from diverse domains and have variant content quality. We curate and release OpenKP, a large scale open domain keyphrase extraction dataset with near one hundred thousand web documents and expert keyphrase annotations. To handle the variations of domain and content quality, we develop BLING-KPE, a neural keyphrase extraction model that goes beyond language understanding using visual presentations of documents and weak supervision from search queries. Experimental results on OpenKP confirm the effectiveness of BLING-KPE and the contributions of its neural architecture, visual features, and search log weak supervision. Zero-shot evaluations on DUC-2001 demonstrate the improved generalization ability of learning from the open domain data compared to a specific domain.
Cite
@article{arxiv.1911.02671,
title = {Open Domain Web Keyphrase Extraction Beyond Language Modeling},
author = {Lee Xiong and Chuan Hu and Chenyan Xiong and Daniel Campos and Arnold Overwijk},
journal= {arXiv preprint arXiv:1911.02671},
year = {2019}
}