I mportance E stimation from M ultiple P erspectives for K eyphrase E xtraction (2021)

Abstract: Keyphrase extraction is a fundamental task in Natural Language Processing, which usually contains two main parts: candidate keyphrase extraction and keyphrase importance estimation. From the view of human understanding documents, we typically measure the importance of phrase according to its syntactic accuracy, information saliency, and concept consistency simultaneously. However, most existing keyphrase extraction approaches only focus on the part of them, which leads to biased results. 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 accuracy, 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 balance the effects of three perspectives. Experimental results on six benchmark datasets show that KIEMP outperforms the existing state-of-the-art keyphrase extraction approaches in most cases.

## introduction keyphrase extraction (ke) aims to select a set of reliable phrases (e.g., "harmonic balance method", "grobner base", "error bound", "algebraic representation", and "singular point" in output / target keyphrases: harmonic balance method; grobner base; error bound; algebraic representation; singular point; quadratic approximation as shown in usually, the candidate set contains much more keyphrases than the ground truth keyphrase set. therefore, it is critical to select the important keyphrase from the candidate set. in other words, keyphrase importance estimation commonly is one of the essential components in many keyphrase extraction models. since the keyphrase extraction task concerns "the automatic selection of important and topical phrases from the body of a document" @xcite . in other words, its goal is to estimate the importance of the candidate keyphrases to determine which one should be extracted. recent approaches @xcite recast the keyphrase extraction as a classification problem, which extracts keyphrases by a binary classifier. however, a binary classifier classifies each candidate keyphrase independently, and consequently, it does not allow us to determine which candidates are better than the others @xcite . therefore, some methods @xcite @xcite propose a ranking model to extract keyphrases, where the goal is to learn a phrase ranker to compare the saliency of two candidate phrases. furthermore, many previous studies @xcite @xcite extract keyphrases with the main topics discussed in the source document, for example, @xcite proposes to build a topical pagerank approach to measure the importance of words concerning different topics. however, most existing keyphrase extraction methods estimate the importance of keyphrases on at most two perspectives, leading to biased extraction. therefore, to improve the performance of keyphrase extraction, the importance of the candidate keyphrases requires to be estimated sufficiently from multiple perspectives. motivated by the phenomenon mentioned above, we propose a new importance estimation from multiple perspectives simultaneously for the keyphrase extraction task. concretely, it estimates the importance from three perspectives with three modules (syntactic accuracy, information saliency, and concept consistency) with three modules. a chunking module, as a binary classification layer, measures the syntactic accuracy of each candidate keyphrase. a ranking module checks the semantics saliency of each candidate phrase by a pairwise ranking approach, which introduces competition between the candidate keyphrases to extract more salient keyphrases. a matching module judges the concept relevance of each candidate phrase in the document via a metric learning framework. furthermore, our model is trained jointly on the above three modules, balancing the effect of three perspectives. experimental results on two benchmark data sets show that kiemp outperforms the existing state-of-the-art keyphrase extraction approaches in most cases. ## related work a good keyphrase extraction system typically consists of two steps: (1) candidate keyphrase extraction, extracting a list of words / phrases that serve as the candidate keyphrases using some heuristics @xcite @xcite @xcite ; and (2) keyphrase importance estimation, determining which of these candidate phrases are keyphrases using different importance estimation approaches. in the candidate keyphrase extraction, the heuristic rules usually are designed to avoid spurious phrases and keep the number of candidates to a minimum @xcite . generally, the heuristics mainly include (1) leverage a stop word list @xcite , (2) allowing words with part-of-speech tags @xcite , (3) composing words to n-grams to be the candidate keyphrases @xcite @xcite . the above heuristics have proven effective with their high recall in extracting gold keyphrases from various sources. motivated by the above methods, in this paper, we leverage cnns to compose words to n-grams as the candidate keyphrases. in the keyphrase importance estimation, the existing methods can be mainly divided into two categories: unsupervised and supervised. the unsupervised method usually are categorized into four groups, i.e., graph-based ranking @xcite , topic-based clustering @xcite , simultaneous learning @xcite , and language modeling @xcite . early supervised approaches to keyphrase extraction recast this task as a binary classification problem @xcite @xcite . later, to determine which candidates are better than the others, many ranking approach is proposed to rank the saliency of two phrases @xcite . this pairwise ranking approach, therefore, introduces competition between candidate keyphrases and has been achieved good performance. both supervised and unsupervised methods construct features or models from different perspectives to measure the importance of candidate keyphrases to determine which keyphrases should be extracted. however, the approaches mentioned earlier consider at most two perspectives when measuring the importance of phrases, which leads to biased keyphrase extraction. different from the existing methods, the proposed kiemp considers estimating the importance of the candidate keyphrases from multiple perspectives simultaneously. ## methodology we formally define the problem of keyphrase extraction as follows. in this paper, kiemp takes a document @xmath0, ..., w i , ..., w m } and learns to extract a set of keyphrases k (each keyphrase may be composed of one or several word(s)) from their n-gram based representations under multiple perspectives. this section describes the architecture of kiemp, as shown in figure 1 . kiemp mainly consists of two submodels: candidate keyphrase extraction and keyphrase importance estimation. the former first identifies and extracts the candidate keyphrases. then the latter estimates the importance of keyphrases from three perspectives simultaneously with three modules to determine which one should be extracted. ## results and analysis this section investigates the performance of the proposed kiemp on six widely-used benchmark datasets (openkp, kp20k, inspec, krapivin, nus, and semeval) from three facets. the first one demonstrates its superiority by comparing it with ten baselines in terms of several metrics. the second one is to verify the sensitivity of the concept dimension. the last one is to explicitly show the keyphrase extraction results of kiemp via two examples (two testing documents). ## conclusions and future work a new keyphrase importance estimation from the multiple perspectives approach is proposed to estimate the importance of keyphrase. benefiting from the designed syntactic accuracy chunking, information saliency ranking, and concept consistency matching modules, kiemp can fairly extract keyphrases. a series of experiments have demonstrated that kiemp outperformed the existing stateof-the-art keyphrase extraction methods. in the hongyuan zha. 2002. generic summarization and keyphrase extraction using mutual reinforcement principle and sentence clustering. in sigir, pages 113-120. acm. haoyu zhang, dingkun long, guangwei xu, pengjun xie, fei huang, and ji wang. 2020. keyphrase extraction with dynamic graph convolutional networks and diversified inference. corr, abs/2010.12828.