Deep Keyphrase Completion
Yu Zhao, Jia Song, Huali Feng, Fuzhen Zhuang, Qing Li, Xiaojie Wang,, Ji Liu

TL;DR
This paper introduces a deep learning-based method called DKPC for keyphrase completion, which generates additional keyphrases for documents by leveraging limited known keyphrases and deep semantic understanding.
Contribution
It proposes a novel encoder-decoder framework that effectively utilizes known keyphrases to improve keyphrase generation, addressing a gap in existing research.
Findings
Outperforms existing methods on benchmark datasets
Effectively incorporates known keyphrases into the generation process
Demonstrates significant improvement in keyphrase recall
Abstract
Keyphrase provides accurate information of document content that is highly compact, concise, full of meanings, and widely used for discourse comprehension, organization, and text retrieval. Though previous studies have made substantial efforts for automated keyphrase extraction and generation, surprisingly, few studies have been made for \textit{keyphrase completion} (KPC). KPC aims to generate more keyphrases for document (e.g. scientific publication) taking advantage of document content along with a very limited number of known keyphrases, which can be applied to improve text indexing system, etc. In this paper, we propose a novel KPC method with an encoder-decoder framework. We name it \textit{deep keyphrase completion} (DKPC) since it attempts to capture the deep semantic meaning of the document content together with known keyphrases via a deep learning framework. Specifically, the…
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Taxonomy
TopicsAdvanced Text Analysis Techniques
