Match More, Extract Better! Hybrid Matching Model for Open Domain Web Keyphrase Extraction (2024)

Abstract: Keyphrase extraction aims to automatically extract salient phrases representing the critical information in the source document. Identifying salient phrases is challenging because there is a lot of noisy information in the document, leading to wrong extraction. To address this issue, in this paper, we propose a hybrid matching model for keyphrase extraction, which combines representation-focused and interaction-based matching modules into a unified framework for improving the performance of the keyphrase extraction task. Specifically, HybridMatch comprises (1) a PLM-based Siamese encoder component that represents both candidate phrases and documents, (2) an interaction-focused matching (IM) component that estimates word matches between candidate phrases and the corresponding document at the word level, and (3) a representation-focused matching (RM) component captures context-aware semantic relatedness of each candidate keyphrase at the phrase level. Extensive experimental results on the OpenKP dataset demonstrate that the performance of the proposed model HybridMatch outperforms the recent state-of-the-art keyphrase extraction baselines. Furthermore, we discuss the performance of large language models in keyphrase extraction based on recent studies and our experiments.

## introduction keyphrase extraction (ke) is a fundamental task in natural language processing that aims to identify a series of salient and representative phrases relevant to the primary information discussed in a source document. specifically, as shown in ## methodology in this section, we describe the architecture of hy-bridmatch. figure 2 shows the proposed model hy-bridmatch, consisting of three components: a plmbased siamese encoder, an interaction-focused matching module, and a representation-focused matching module. in the following sections, the details of hybridmatch are given. ## experimental settings in this section, we first introduce the used dataset. then, we present the evaluation metric, baselines, and implementation details. ## results and analysis in this section, we show the results of the baselines and our proposed models. we also show the analyses of hybridmatch in different scenarios, including an ablation test and a case study. in addition, we also discuss the performance of large language models in keyphrase extraction based on recent studies and our experiments. ## related work typically, keyphrase extraction aims to automatically extract a set of phrases related to the primary information discussed in the source document @xcite @xcite . specifically, recent keyphrase extraction baselines can be mainly divided into two components: candidate keyphrase extraction and keyphrase importance estimation. the former uses heuristics to select a candidate keyphrase set @xcite @xcite @xcite @xcite for the given document, and the latter is to measure the importance of candidates for extracting keyphrases @xcite @xcite @xcite . unlike the existing methods, in this paper, we combine interaction-focused and representationfocused matching into a unified matching model for extracting keyphrases. ## conclusion in this paper, we propose a new hybrid matching model (hybridmatch) to combine representationfocused and interaction-focused matching modules into a unified model. to filter the noisy information, we first design an interaction-focused matching module and a representation-focused matching module to estimate the important scores of candidate phrases from different granularities. then, we aggregate the relevance matching scores from im and rm as the importance scores to rank and extract keyphrases. extensive experiments demonstrate the effectiveness of our model, and ablation studies verify the performance of each component. in addition, we discuss the performance of large language models in keyphrase extraction based on recent studies and our experiments. in the future, it will be interesting to introduce a graph-based network to capture the interactions from multiple granularities to calculate the phrase-document relevance as the importance of each candidate for improving the performance of keyphrase extraction.