Importance Estimation from Multiple Perspectives for Keyphrase Extraction

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 2726–2736 November 7–11, 2021. c⃝2021 Association for Computational Linguistics 2726 Importance Estimation from Multiple Perspectives for Keyphrase Extraction Mingyang Song, Liping Jing∗and Lin Xiao Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University, China {mingyang.song, lpjing, 17112079}@bjtu.edu.cn Abstract Keyphrase extraction is a fundamental task in Natural Language Processing, which usually contains two main parts: candidate keyphrase extraction and keyphrase importance estima- tion. From the view of human understanding documents, we typically measure the impor- tance of phrase according to its syntactic ac- curacy, information saliency, and concept con- sistency simultaneously. However, most exist- ing keyphrase extraction approaches only focus on the part of them, which leads to biased re- sults. In this paper, we propose a new approach to estimate the importance of keyphrase from multiple perspectives (called as KIEMP) and further improve the performance of keyphrase extraction. Specifically, KIEMP estimates the importance of phrase with three modules: a chunking module to measure its syntactic accu- racy, a ranking module to check its information saliency, and a matching module to judge the concept (i.e., topic) consistency between phrase and the whole document. These three modules are seamlessly jointed together via an end-to- end multi-task learning model, which is helpful for three parts to enhance each other and bal- ance the effects of three perspectives. Experi- mental results on six benchmark datasets show that KIEMP outperforms the existing state-of- the-art keyphrase extraction approaches in most cases. 1 Introduction Keyphrase Extraction (KE) aims to select a set of reliable phrases (e.g., “harmonic balance method", “grobner base", “error bound", “algebraic repre- sentation", and “singular point" in Table 1) with salient information and central topics from a given document, which is a fundamental task in natu- ral language processing. Most classic keyphrase extraction methods typically include two mainly components: candidate keyphrase extraction and ∗Corresponding author. Input Document: harmonic balance ( hb ) method is well known principle for analyzing periodic oscillations on nonlinear networks and systems. because the hb method has a truncation error, approximated solutions have been guaranteed by error bounds. however, its numerical computation is very time consuming compared with solving the hb equation. this paper proposes proposes an algebraic representation of the error bound using grobner base. the algebraic representation enables to decrease the computational cost of the error bound considerably. moreover, using singular points of the algebraic representation, we can obtain accurate break points of the error bound by collisions. Output / Target Keyphrases: harmonic balance method; grobner base; error bound; algebraic representation; singular point; quadratic ap- proximation Table 1: Sample input document with output / target keyphrases in KP20k testing set. Specially, keyphrases typically can be categorized into two types: present keyphrase that appears in a given document and absent keyphrase which does not appear in a given document. keyphrase importance estimation (Medelyan et al., 2009; Liu et al., 2010; Hasan and Ng, 2014). As shown in Table 1, each keyphrase usually con- …

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