Mitigating Over-Generation for Unsupervised Keyphrase Extraction with Heterogeneous Centrality Detection (2023)

Abstract: Over-generation errors occur when a keyphrase extraction model correctly determines a candidate keyphrase as a keyphrase because it contains a word that frequently appears in the document but at the same time erroneously outputs other candidates as keyphrases because they contain the same word. To mitigate this issue, we propose a new heterogeneous centrality detection approach (CentralityRank), which extracts keyphrases by simultaneously identifying both implicit and explicit centrality within a heterogeneous graph as the importance score of each candidate. More specifically, CentralityRank detects centrality by taking full advantage of the content within the input document to construct graphs that encompass semantic nodes of varying granularity levels, not limited to just phrases. These additional nodes act as intermediaries between candidate keyphrases, enhancing cross-phrase relations. Furthermore, we introduce a novel adaptive boundary-aware regularization that can leverage the position information of candidate keyphrases, thus influencing the importance of candidate keyphrases. Extensive experimental results demonstrate the superiority of CentralityRank over recent state-of-the-art unsupervised keyphrase extraction baselines across three benchmark datasets.

## introduction keyphrase extraction (ke) is the task of extracting a set of salient and relevant phrases (e.g., "information extraction", "natural language processing", "ontology", "intelligent analysis", and "semantic analysis" in part of the input document: information extraction using natural language processing tools focuses on extracting explicitly stated information from textual material (...) to perform intelligent analysis on the information, we provide an ontology, which describes the domain of the extracted information, in addition to rules that govern the classification and interpretation of added elements. the ontology is at the core of an interactive system that assists analysts with the collection, extraction, organization, analysis and retrieval of information, with the topic of "terrorism financing" as a case study. user interaction provides valuable assistance in assigning meaning to extracted information. the system is designed as a set of tools to provide the user with the flexibility and power (...) ## experiments we conduct experiments to demonstrate the effectiveness of our proposed centralityrank model. in this section, we introduce our experimental settings, including datasets, evaluation metrics, baselines, and implementation details. ## results and analysis based on the results illustrated in figure 2 , it becomes evident that both the implicit and explicit centrality detection modules contribute significantly to enhancing model performance in most cases. this observation reinforces the importance of assessing the importance scores of candidate keyphrases from multiple perspectives. moreover, the results also illustrate that employing intermediaries to influence the relationship modeling among candidate keyphrases within a graph, consequently affecting the estimation of importance, represents an effective strategy. ## related work most of the existing keyphrase extraction models can be broadly categorized into four main groups: statistics-based, topic-based, graph-based, and embedding-based models. statistics-based models @xcite generally identify keyphrases by estimating the importance of candidate keyphrases through various statistical features. these features may include word frequency, phrase position, linguistic characteristics of natural language, and more. topic-based models @xcite typically leverage topic information to determine whether a candidate keyphrase qualifies as a keyphrase. they often involve methods that consider the topical relevance of keyphrases to the document. graph-based models @xcite represent the input document as a graph, where nodes correspond to words or phrases, and edges indicate relationships between them. candidate keyphrases are ranked based on graph-based measures such as centrality or similarity. embeddingbased models @xcite ) make use of pre-trained word embeddings to capture semantic representations of keyphrases. then, they calculate the importance of each candidate keyphrase by its representation. these categories encompass a wide range of techniques and approaches for keyphrase extraction, each with its strengths and limitations. researchers in the field often choose a specific type or combine elements from multiple categories to develop effective keyphrase extraction models. unlike the existing models, to address the overgeneration error, our model selects keyphrases with less redundancy by detecting implicit and explicit centrality within a heterogeneous graph to filter noises in the input document. ## conclusion in this paper, we propose a heterogeneous centrality detection model, which incorporates implicit and explicit centrality to identify and select keyphrases in the source document. additionally, we leverage the position information of candidate keyphrases to optimize the discourse-aware importance score of each candidate keyphrase. our experiments yield compelling results that highlight the superiority of our model when compared to existing sota keyphrase extraction baselines. we validate our model on three benchmark datasets, demonstrating its robust performance and effectiveness in improving keyphrase extraction quality.