Deep Keyphrase Generation
By Rui Meng, Sanqiang Zhao, Shuguang Han, Daqing He, Peter Brusilovsky, Yu Chi. Annual Meeting of the Association for Computational Linguistics, 2017
369 citations recorded by the source corpus.
Keyphrase provides highly-summative information that can be effectively used for understanding, organizing and retrieving text content. Though previous studies have provided many workable solutions for automated keyphrase extraction, they commonly divided the to-be-summarized content into multiple text chunks, then ranked and selected the most meaningful ones. These approaches could neither identify keyphrases that do not appear in the text, nor capture the real semantic meaning behind the text. We propose a generative model for keyphrase prediction with an encoder-decoder framework, which can effectively overcome the above drawbacks. We name it as deep keyphrase generation since it attempts to capture the deep semantic meaning of the content with a deep learning method. Empirical analysis on six datasets demonstrates that our proposed model not only achieves a significant performance boost on extracting keyphrases that appear in the source text, but also can generate absent keyphrases based on the semantic meaning of the text. Code and dataset are available at https://github.com/memray/seq2seq-keyphrase.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/78b47ef088d537702a2e13297d8b58ee55a3a5cd
How researchers see this paper
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In Artificial Intelligence
- Furu Wei
- Mohit Bansal
- Jianfeng Gao
- Adam Trischler
- M. Zhou
- Hannaneh Hajishirzi
- Yoshua Bengio
- Percy Liang
Citations in the corpus
- Keyphrase Generation for Scientific Document Retrieval (2020, methodology): “In this study, we consider the following two neural keyphrase generation models: seq2seq + copy (Meng et al., 2017) is a sequence-to-sequence model with attention, augmented with a copying mechanism (Gu et al., 2016) to predict phrases that rarely occur.”
- Structure-Augmented Keyphrase Generation (2021, methodology): “Thus, we formulate our problem as keyphrase generation (KG) (Meng et al., 2017) of predicting keyphrases, including absent words as well as present words, adopting encoder-decoder architecture.”
- Neural Models for Key Phrase Extraction and Question Generation (2018, background): “Recently, Meng et al. (2017) proposed a generative model for key phrase prediction with an encoder-decoder framework that is able both to generate words from a vocabulary and point to words from the document.”
- Neural Open Information Extraction (2018, background): “, 2017) and recently keyphrase extraction (Meng et al., 2017).”