CDM: Combining Extraction and Generation for Definition Modeling

• URL: http://arxiv.org/abs/2111.07267v1
• Date: Sun, 14 Nov 2021 08:03:18 GMT
• Title: CDM: Combining Extraction and Generation for Definition Modeling
• Authors: Jie Huang, Hanyin Shao, Kevin Chen-Chuan Chang
• Abstract summary: We propose to combine extraction and generation for definition modeling. First extract self- and correlative definitional information of target terms from the Web. Then generate the final definitions by incorporating the extracted definitional information.
• Score: 8.487707405248242
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Definitions are essential for term understanding. Recently, there is an increasing interest in extracting and generating definitions of terms automatically. However, existing approaches for this task are either extractive or abstractive - definitions are either extracted from a corpus or generated by a language generation model. In this paper, we propose to combine extraction and generation for definition modeling: first extract self- and correlative definitional information of target terms from the Web and then generate the final definitions by incorporating the extracted definitional information. Experiments demonstrate our framework can generate high-quality definitions for technical terms and outperform state-of-the-art models for definition modeling significantly.

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