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SGG: Learning to Select, Guide and Generate for Keyphrase Generation - 2021

Sgg: Learning To Select, Guide And Generate For Keyphrase Generation

Research Paper on Sgg: Learning To Select, Guide And Generate For Keyphrase Generation

Research Area:  Machine Learning

Abstract:

Keyphrases, that concisely summarize the high-level topics discussed in a document, can be categorized into present keyphrase which explicitly appears in the source text, and absent keyphrase which does not match any contiguous subsequence but is highly semantically related to the source. Most existing keyphrase generation approaches synchronously generate present and absent keyphrases without explicitly distinguishing these two categories. In this paper, a Select-Guide-Generate (SGG) approach is proposed to deal with present and absent keyphrase generation separately with different mechanisms. Specifically, SGG is a hierarchical neural network which consists of a pointing-based selector at low layer concentrated on present keyphrase generation, a selection-guided generator at high layer dedicated to absent keyphrase generation, and a guider in the middle to transfer information from selector to generator. Experimental results on four keyphrase generation benchmarks demonstrate the effectiveness of our model, which significantly outperforms the strong baselines for both present and absent keyphrases generation. Furthermore, we extend SGG to a title generation task which indicates its extensibility in natural language generation tasks.

Keywords:  
Keyphrase Generation
Deep Learning
Machine Learning

Author(s) Name:  Jing Zhao, Junwei Bao, Yifan Wang, Youzheng Wu, Xiaodong He, Bowen Zhou

Journal name:  Computer Science

Conferrence name:  

Publisher name:  arXiv:2105.02544

DOI:  10.48550/arXiv.2105.02544

Volume Information: