Local word vectors guiding keyphrase extraction

Papagiannopoulou, Eirini; Tsoumakas, Grigorios

doi:10.1016/j.ipm.2018.06.004

Cited by 77 publications

(45 citation statements)

References 29 publications

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“…This method can clearly distinguish the target document from others. Papagiannopoulou and Tsoumaka proposed RVA [24], a local word vectors guiding keyphrase extraction model, which uses the average of all the candidate phrases' embeddings trained on individual files with GloVe as the reference vector, and then the similarity between the embeddings of candidate keyphrase and the reference vector is calculated and used as the score to rank. Bennani-Smires et al proposed EmbedRank [25], which uses the cosine similarity between the embeddings of candidate keyphrase and the sentence embeddings of the document.…”

Section: Embedding-based Keyphrase Extractionmentioning

confidence: 99%

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SIFRank: A New Baseline for Unsupervised Keyphrase Extraction Based on Pre-Trained Language Model

et al. 2020

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In the age of social media, faced with a huge amount of knowledge and information, accurate and effective keyphrase extraction methods are needed to be applied in information retrieval and natural language processing. It is difficult for traditional keyphrase extraction models to contain a large amount of external knowledge information, but with the rise of pre-trained language models, there is a new way to solve this problem. Based on the above background, we propose a new baseline for unsupervised keyphrase extraction based on pre-trained language model called SIFRank. SIFRank combines sentence embedding model SIF and autoregressive pre-trained language model ELMo, and it has the best performance in keyphrase extraction for short documents. We speed up SIFRank while maintaining its accuracy by document segmentation and contextual word embeddings alignment. For long documents, we upgrade SIFRank to SIFRank+ by position-biased weight, greatly improve its performance on long documents. Compared to other baseline models, our model achieves state-of-the-art level on three widely used datasets. INDEX TERMS Keyphrase extraction, pre-trained language model, sentence embeddings, position-biased weight, SIFRank. I. INTRODUCTION Keyphrase extraction is the task of selecting a set of words or phrases from a document that could summarize the main topics discussed in the document [1]. Keyphrase extraction can greatly accelerate the speed of information retrieval, help people get the first-hand information from a long text quickly and accurately. A. MOTIVATION Keyphrase Extraction can be divided into two main kinds of approaches: supervised and unsupervised. Supervised methods perform better on specific domain tasks, but it takes a lot of labor to annotate the corpus, and the model after training may overfit and do not work well on other datasets. The main traditional unsupervised methods are mainly divided into the models based on statistics and the models based on The associate editor coordinating the review of this manuscript and approving it for publication was Shuai Han .

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Section: Embedding-based Keyphrase Extractionmentioning

confidence: 99%

“…The graph-based models 2 are TextRank [3], SingleRank [10], TopicRank [11], Position-Rank [13] and Multipartite [12]. The embedding-based models are RVA [24] and EmbedRank [25].…”

Section: B Compare With Other Baselinesmentioning

confidence: 99%

SIFRank: A New Baseline for Unsupervised Keyphrase Extraction Based on Pre-Trained Language Model

et al. 2020

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“…Furthermore, RVA's performance witnesses the effectiveness of keyphrase extraction from scientific publications using only their titles and abstracts. Papagiannopoulou and Tsoumakas () conducted experiments using the same evaluation approach (Rousseau & Vazirgiannis, ) and two versions of each unsupervised keyphrase extraction method, one with the title/abstract and one with the full‐text of each article. The abstract version of both RVA and the graph‐based methods is better than the fulltext version according to the partial match evaluation approach, possibly due to the redundancy which is included in the fulltexts.…”

Section: Empirical Evaluation Studymentioning

confidence: 99%

A review of keyphrase extraction

Papagiannopoulou

Tsoumakas

2019

WIREs Data Min & Knowl

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133

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Keyphrase extraction is a textual information processing task concerned with the automatic extraction of representative and characteristic phrases from a document that express all the key aspects of its content. Keyphrases constitute a succinct conceptual summary of a document, which is very useful in digital information management systems for semantic indexing, faceted search, document clustering and classification. This article introduces keyphrase extraction, provides a well‐structured review of the existing work, offers interesting insights on the different evaluation approaches, highlights open issues and presents a comparative experimental study of popular unsupervised techniques on five datasets. This article is categorized under: Ensemble Methods > Web Mining Ensemble Methods > Text Mining

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“…Topics are defined as single and multiword with significant similarity. With the extensive use of deep learning in various fields [36], many new models have also been proposed in the field of keywords extraction [17], [37]- [39], and achieved the best results in many datasets. However, many keywords extraction methods based on deep learning are generally supervised, and the model training will take a long time, so it will not be compared with the methods proposed in this article.…”

Section: Related Workmentioning

confidence: 99%

Automatic Keywords Extraction Based on Co-Occurrence and Semantic Relationships Between Words

Mao¹,

Huang²,

Li³

et al. 2020

IEEE Access

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Automatic keywords extraction is a method that extracts words or phrases from a document which can express the main idea of the document. In this paper, we propose an unsupervised keywords extraction framework for individual documents, which improves the keywords extraction from two aspects. In the step of candidate keywords selection, we use the methods of removing the stopwords, regular matching, and length filtering to reduce the number of candidate keywords, but improve the quality. In the step of scoring words, we use word co-occurrence, semantic relationships (WordNet, Word Embedding, Normalized Google Distance), and three ways to combine word co-occurrence and semantic relationships to measure the weight of edges in the graph model. In experiments, we use Precision, Recall, and F1-measure values as evaluation criteria to compare all keywords extraction methods we proposed with other strong baseline methods in two datasets. According to the results of experiments, methods under our proposed framework achieve good results. We verify that the methods of using both word co-occurrence and semantic relationships have a better effect on keywords extraction than using co-occurrence or semantic relationships only. At the same time, we also find that for the keywords extraction of individual documents, the method of using co-occurrence between words has a better effect than semantic relationships.

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Local word vectors guiding keyphrase extraction

Cited by 77 publications

References 29 publications

SIFRank: A New Baseline for Unsupervised Keyphrase Extraction Based on Pre-Trained Language Model

SIFRank: A New Baseline for Unsupervised Keyphrase Extraction Based on Pre-Trained Language Model

A review of keyphrase extraction

Automatic Keywords Extraction Based on Co-Occurrence and Semantic Relationships Between Words

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