Automatic Keyphrase Extraction Research Articles

Utilizing cognitive signals generated during human reading to enhance keyphrase extraction from microblogs

Microblogging platforms have seen exponential growth, leading to an abundance of user-generated content. The challenge now is to efficiently extract crucial information from this vast and dispersed text data. It also serves as the goal of our research on Automatic Keyphrase Extraction (AKE) for microblog. Eye-tracking signals, that reflect users' tendency to prioritize certain words while reading, have been employed to enhance AKE performance from microblogs. However, relying solely on eye-tracking has its limitations owing to constraints in physiological mechanism support, acquisition techniques, and feature decoding. Consequently, we propose the integration of electroencephalogram (EEG) signals with eye-tracking signals to improve microblogs-based AKE, thereby overcoming the aforementioned limitations. Our first step is identifying specific features present in cognitive signals generated during human reading. We selected EEG signals (8 features) and eye-tracking signals (17 features) from the cognitive language processing corpus ZUCO, to examine the efficacy when they are combined with the microblogs-based AKE. To avoid cognitive signal distortion by certain model structures, we introduced these signals at the inputs of the soft attention layer and at the query vectors of the self-attention layer. For evaluation, we performed several AKE tests on microblogs with various combinations of cognitive signals. The results demonstrate a consistent enhancement in the performance of AKE due to cognitive signals generated during human reading, regardless of different feature combinations and models. Specifically, EEG signals exhibited the most significant improvement. However, combining EEG signals with eye-tracking signals yielded results that fell between the performance levels of the two signal types, indicating that their integration might have some synergistic effects. Further investigation is needed to understand the underlying mechanisms responsible for this outcome. The code and dataset for this paper can be accessed at https://github.com/yan-xinyi/AKE.

Contextual topic discovery using unsupervised keyphrase extraction and hierarchical semantic graph model

Recent technological advancements have led to a significant increase in digital documents. A document’s key information is generally represented by the keyphrases that provide the abstract description contained therein. With traditional keyphrase techniques, however, it is difficult to identify relevant information based on context. Several studies in the literature have explored graph-based unsupervised keyphrase extraction techniques for automatic keyphrase extraction. However, there is only limited existing work that embeds contextual information for keyphrase extraction. To understand keyphrases, it is essential to grasp both the concept and the context of the document. Hence, a hybrid unsupervised keyphrase extraction technique is presented in this paper called ContextualRank, which embeds contextual information such as sentences and paragraphs that are relevant to keyphrases in the keyphrase extraction process. We propose a hierarchical topic modeling approach for topic discovery based on aggregating the extracted keyphrases from ContextualRank. Based on the evaluation on two short-text datasets and one long-text dataset, ContextualRank obtains remarkable improvements in performance over other baselines in the short-text datasets.

BibRank: Automatic Keyphrase Extraction Platform Using Metadata

Automatic Keyphrase Extraction involves identifying essential phrases in a document. These keyphrases are crucial in various tasks, such as document classification, clustering, recommendation, indexing, searching, summarization, and text simplification. This paper introduces a platform that integrates keyphrase datasets and facilitates the evaluation of keyphrase extraction algorithms. The platform includes BibRank, an automatic keyphrase extraction algorithm that leverages a rich dataset obtained by parsing bibliographic data in BibTeX format. BibRank combines innovative weighting techniques with positional, statistical, and word co-occurrence information to extract keyphrases from documents. The platform proves valuable for researchers and developers seeking to enhance their keyphrase extraction algorithms and advance the field of natural language processing.

Automatic keyphrases extraction: an overview of deep learning approaches

Automatic keyphrases extraction (AKE) is a principal task in natural language processing (NLP). Several techniques have been exploited to improve the process of extracting keyphrases from documents. Deep learning (DL) algorithms are the latest techniques used in prediction and extraction of keyphrases. DL is one of the most complex types of machine learning, relying on the use of artificial neural networks to make the machine follow the same decision-making path as the human brain. In this paper, we present a review of deep learning-based methods for AKE from documents, to highlight their contribution to improving keyphrase extraction performance. This review will also provide researchers with a collection of data and information on the mechanisms of deep learning algorithms in the AKE domain. This will allow them to solve problems encountered by AKE approaches and propose new methods for improving key-extraction performance.

Present and absent keyphrases extraction: an approach based on sentence embedding

The automatic keyphrases extraction (AKE) of a document is any expression by which we can learn its content without having to read it. Keyphrases are exploited in natural language processing (NLP) applications. These phrases are often mentioned in the document but there may be some keyphrases that are not mentioned. In the field of AKE, researchers have exploited many techniques, such as statistical calculation, deep learning algorithms, graph representation, and sentence embedding techniques. Approaches that exploit embedding techniques calculate the similarity between a document and a candidate keyphrase, where similar phrases to the document are considered as keyphrases. Representing the document by a single vector makes its performance poor, especially in long documents. This is in addition to the inability of these methods to generate absent keyphrases. In order to overcome these problems, our paper proposes an unsupervised approach to AKE, based on the universal sentence encoder (USE) to represent candidate keyphrases and parts of the document probably containing keyphrases. Our method also generates keyphrases not mentioned in the text. We compared the performance of the proposed approach with other methods based on embedding techniques, where the results showed the superiority of our approach especially in long documents.

A Systematic Literature Review of Keyphrases Extraction Approaches

The keyphrases of a document are the textual units that characterize its content such as the topics it addresses, its ideas, their field, etc. Thousands of books, articles and web pages are published every day. Manually extracting keyphrases is a tedious task and takes a lot of time. Automatic keyphrases extraction is an area of text mining that aims to identify the most useful and important phrases that give meaning to the content of a document. Keyphrases can be used in many Natural Language Processing (NLP) applications, such as text summarization, text clustering and text classification. This article provides a Systematic Literature Review (SLR) to investigate, analyze, and discuss existing relevant contributions and efforts that use new concepts and tools to improve keyphrase extraction. We have studied the supervised and unsupervised approaches to extracting keyphrases published in the period 2015-2022. We have also identified the steps most commonly used by the different approaches. Additionally, we looked at the criteria that should be evaluated to improve the accuracy of keyphrases extraction. Each selected approach was evaluated for its ability to extract keyphrases. Our findings highlight the importance of keyphrase extraction, and provide researchers and practitioners with information about proposed solutions and their limitations, which contributes to extract keyphrases in a powerful and meaningful way effective.

Enhancing keyphrase extraction from academic articles with their reference information

With the development of Internet technology, the phenomenon of information overload is becoming more and more obvious. It takes a lot of time for users to obtain the information they need. However, keyphrases that summarize document information highly are helpful for users to quickly obtain and understand documents. For academic resources, most existing studies extract keyphrases through the title and abstract of papers. We find that title information in references also contains author-assigned keyphrases. Therefore, this article uses reference information and applies two typical methods of unsupervised extraction methods (TF*IDF and TextRank), two representative traditional supervised learning algorithms (Naïve Bayes and Conditional Random Field) and a supervised deep learning model (BiLSTM-CRF), to analyze the specific performance of reference information on keyphrase extraction. It is expected to improve the quality of keyphrase recognition from the perspective of expanding the source text. The experimental results show that reference information can increase precision, recall, and F1 of automatic keyphrase extraction to a certain extent. This indicates the usefulness of reference information on keyphrase extraction of academic papers and provides a new idea for the research on automatic keyphrase extraction.

HAKE: an Unsupervised Approach to Automatic Keyphrase Extraction for Multiple Domains

HAKE: an Unsupervised Approach to Automatic Keyphrase Extraction for Multiple Domains

ISKE: An unsupervised automatic keyphrase extraction approach using the iterated sentences based on graph method

The automatic extraction of key information is necessary for knowledge discovery in this era of rapid knowledge growth. The extraction of key information can also help researchers quickly obtain the information they want instead of reading through all potential documents. Recently, researchers have refocused their attention from words to sentences because utilizing sentences outperforms with respect to illustrating semantics and reduces the calculation complexity. We present a novel and lightweight automatic keyphrase extraction algorithm that does not depend on any external resources, including an external dictionary or corpus. Unlike traditional graph-based algorithms that iterate words to generate keyphrase lists, our proposal uses iterated sentences to rank words and generate keyphrase lists for the semantic information of sentences that are more complete than the word. We initialize the values of words with weighted information and generate a sentence score using these values. Then, we integrate sentences to update their values; hence, the values of the words are updated with the sentence information. We iterate this process until the values of the sentences and words converge. The proposed method is based on a measurement of the relations between sentences and an evaluation of the flow of these relations in an easily understood manner. These relationships are based on the hypothesis that the causality between adjacent sentences is semantically stronger than the causality between words. We not only increase the extraction accuracy, but also reduce the number of iterations of the algorithm. We compare our proposed method with five strong, popular baseline algorithms on four datasets. The results show that our proposed method performs better than the other algorithms on three evaluation metrics.

Automatic Keyphrase Extraction from Scientific Chinese Medical Abstracts Based on Character-Level Sequence Labeling

AbstractPurposeAutomatic keyphrase extraction (AKE) is an important task for grasping the main points of the text. In this paper, we aim to combine the benefits of sequence labeling formulation and pretrained language model to propose an automatic keyphrase extraction model for Chinese scientific research.Design/methodology/approachWe regard AKE from Chinese text as a character-level sequence labeling task to avoid segmentation errors of Chinese tokenizer and initialize our model with pretrained language model BERT, which was released by Google in 2018. We collect data from Chinese Science Citation Database and construct a large-scale dataset from medical domain, which contains 100,000 abstracts as training set, 6,000 abstracts as development set and 3,094 abstracts as test set. We use unsupervised keyphrase extraction methods including term frequency (TF), TF-IDF, TextRank and supervised machine learning methods including Conditional Random Field (CRF), Bidirectional Long Short Term Memory Network (BiLSTM), and BiLSTM-CRF as baselines. Experiments are designed to compare word-level and character-level sequence labeling approaches on supervised machine learning models and BERT-based models.FindingsCompared with character-level BiLSTM-CRF, the best baseline model with F1 score of 50.16%, our character-level sequence labeling model based on BERT obtains F1 score of 59.80%, getting 9.64% absolute improvement.Research limitationsWe just consider automatic keyphrase extraction task rather than keyphrase generation task, so only keyphrases that are occurred in the given text can be extracted. In addition, our proposed dataset is not suitable for dealing with nested keyphrases.Practical implicationsWe make our character-level IOB format dataset of Chinese Automatic Keyphrase Extraction from scientific Chinese medical abstracts (CAKE) publicly available for the benefits of research community, which is available at:https://github.com/possible1402/Dataset-For-Chinese-Medical-Keyphrase-Extraction.Originality/valueBy designing comparative experiments, our study demonstrates that character-level formulation is more suitable for Chinese automatic keyphrase extraction task under the general trend of pretrained language models. And our proposed dataset provides a unified method for model evaluation and can promote the development of Chinese automatic keyphrase extraction to some extent.

TripleRank: An unsupervised keyphrase extraction algorithm

Automatic keyphrase extraction algorithms aim to identify words and phrases that contain the core information in documents. As online scholarly resources have become widespread in recent years, better keyphrase extraction techniques are required to improve search efficiency. We present two features, keyphrase semantic diversity and keyphrase coverage, to overcome limitations of existing methods for unsupervised keyphrase extraction. Keyphrase semantic diversity is the degree of semantic variety in the extraction result, which is introduced to avoid extracting synonym phrases that contain the same high-score candidate. Keyphrase coverage refers to candidates’ representativeness of other words in documents. We propose an unsupervised keyphrase extraction method called TripleRank, which evaluates three features: word position (a sensitive feature for academic documents) and two innovative features mentioned above. The architecture of TripleRank includes three sub-models that score the three features and a summing model. Though involving multiple models, there is no typical iteration process in TripleRank; hence, the computational cost is relatively low. TripleRank has led the experiment results on four academic datasets compared to four state-of-the-art baseline models, which confirmed the influence of keyphrase semantic diversity and keyphrase coverage and proved the efficiency of our method.

Lexical Patterns Based on Maximal Frequent Secuences for Automatic Keyphrase Extraction

This paper presents a method for the automatic keyphrase extraction task using lexical patterns. First, the patterns are obtained from a set of data and converted into regular expression search patterns, allowing to consider sequences of characters that define a phrase without depending on its syntactic or semantic characteristics and thus obtain a list of possible candidates. Besides, to select the best, only those that obtained a high weight will be considered, in the following four weights: Boolean (B), Precision (P), Recall (R), and F-Measure (F); which corresponds to the result obtained from each evaluated pattern, therefore a list is generating of the best 5,10 and 15 keyphrases for each document. The evaluation of the method was realized by length (L) and combination (C), where the combination takes the best candidates for each length (1 to 4). The method was tested in corpus of scientific articles using the SemEval-2010 data set for task 5.

Analysis of OWA operators for automatic keyphrase extraction in a semantic context

Automatic keyphrase extraction from texts is useful for many computational systems in the fields of natural language processing and text mining. Although a number of solutions to this problem have been described, semantic analysis is one of the least exploited linguistic features in the most widely-known proposals, causing the results obtained to have low accuracy and performance rates. This paper presents an unsupervised method for keyphrase extraction, based on the use of lexico-syntactic patterns for extracting information from texts, and a fuzzy topic modeling. An OWA operator combining several semantic measures was applied to the topic modeling process. This new approach was evaluated with Inspec and 500N-KPCrowd datasets. Several approaches within our proposal were evaluated against each other. A statistical analysis was performed to substantiate the best approach of the proposal. This best approach was also compared with other reported systems, giving promising results.

A Review of Unsupervised Keyphrase Extraction Methods Using Within-Collection Resources

An essential part of a text generation task is to extract critical information from the text. People usually obtain critical information in the text via manual extraction; however, the asymmetry between the ability to process information manually and the speed of information growth makes it impossible. This problem can be solved by automatic keyphrase extraction. In this paper, the mainstream unsupervised methods to extract keyphrases are summarized, and we analyze in detail the reasons for the differences in the performance of methods then provided some solutions.

Automatic Concept Extraction for Domain and Student Modeling in Adaptive Textbooks

The increasing popularity of digital textbooks as a new learning media has resulted in a growing interest in developing a new generation of adaptive textbooks that can help readers to learn better through adapting to the readers’ learning goals and the current state of knowledge. These adaptive textbooks are most frequently powered by internal knowledge models, which associate a list of unique domain knowledge concepts with each section of the textbook. With this kind of concept-level knowledge representation, a number of intelligent operations could be performed, which include student modeling, adaptive navigation support, and content recommendation. However, manual indexing of each textbook section with concepts is challenging, time-consuming, and prone to errors. Modern research in the area of natural language processing offers an attractive alternative, called automatic keyphrase extraction. While a range of keyphrase and concept extraction methods have been developed over the last twenty years, few of the known approaches were applied and evaluated in a textbook context. In this paper, we present FACE, a supervised feature-based machine learning method for automatic concept extractions from digital textbooks. This method has been created for building domain and student models that form the core of intelligent textbooks. We evaluated FACE on a newly constructed full-scale dataset by assessing how well it approximates concept annotations produced by human experts and how well it supports the needs of student modeling. The results show that FACE outperforms several state-of-the-art keyphrase extraction methods.

Semantic Unsupervised Automatic Keyphrases Extraction by Integrating Word Embedding with Clustering Methods

Increasingly, the web produces massive volumes of texts, alone or associated with images, videos, photographs, together with some metadata, indispensable for their finding and retrieval. Keywords/keyphrases that characterize the semantic content of documents should be, automatically or manually, extracted, and/or associated with them. The paper presents a novel method to address the problem of the automatic unsupervised extraction of keywords/phrases from texts, expressed both in English and in Italian. The main feature of this approach is the integration of two methods that have given interesting results: word embedding models, such as Word2Vec or GloVe able to capture the semantics of words and their context, and clustering algorithms, able to identify the essence of the terms and choose the more significant one(s), to represent the contents of a text. In the paper, the datasets used are presented, together with the method implemented and the results obtained. These results will be discussed, commented, and compared with those obtained in previous experimentations, using TextRank, Rapid Automatic Keyword Extraction (RAKE), and TF-IDF.

TOP-Rank: A TopicalPostionRank for Extraction and Classification of Keyphrases in Text

Keyphrase extraction is the task of extracting the most important phrases from a document. Automatic keyphrase extraction attempts to itemize a document content as metainformation and facilitate efficient information retrieval. In this paper we propose TOP-Rank, an approach for keyphrase extraction and keyphrase classification. For keyphrase extraction, we build an approach based on the position of keyphrases in the document and expand it with topical ranking of keyphrases. In particular, keyphrase extraction technique analyzes the documents and extracts keyphrases from the document by giving a higher rank to topical phrases. After keyphrase extraction, we classify keyphrases as process, material and task. Our evaluation on diverse datasets shows that TOP-Rank achieves F1-score of 0.73 for keyphrase classification improving upon state-of-the-art methods by a huge margin.

TeKET: a Tree-Based Unsupervised Keyphrase Extraction Technique

Automatic keyphrase extraction techniques aim to extract quality keyphrases for higher level summarization of a document. Majority of the existing techniques are mainly domain-specific, which require application domain knowledge and employ higher order statistical methods, and computationally expensive and require large train data, which is rare for many applications. Overcoming these issues, this paper proposes a new unsupervised keyphrase extraction technique. The proposed unsupervised keyphrase extraction technique, named TeKET or Tree-based Keyphrase Extraction Technique, is a domain-independent technique that employs limited statistical knowledge and requires no train data. This technique also introduces a new variant of a binary tree, called KeyPhrase Extraction (KePhEx) tree, to extract final keyphrases from candidate keyphrases. In addition, a measure, called Cohesiveness Index or CI, is derived which denotes a given node’s degree of cohesiveness with respect to the root. The CI is used in flexibly extracting final keyphrases from the KePhEx tree and is co-utilized in the ranking process. The effectiveness of the proposed technique and its domain and language independence are experimentally evaluated using available benchmark corpora, namely SemEval-2010 (a scientific articles dataset), Theses100 (a thesis dataset), and a German Research Article dataset, respectively. The acquired results are compared with other relevant unsupervised techniques belonging to both statistical and graph-based techniques. The obtained results demonstrate the improved performance of the proposed technique over other compared techniques in terms of precision, recall, and F1 scores.

Learning keyphrases from corpora and knowledge models

AbstractExtraction keyphrase systems traditionally use classification algorithms and do not consider the fact that part of the keyphrases may not be found in the text, reducing the accuracy of such algorithms a priori. In this work, we propose to improve the accuracy of these systems with inferential mechanisms that use a knowledge representation model, including symbolic models of knowledge bases and distributional semantics, to expand the set of keyphrase candidates to be submitted to the classification algorithm with terms that are not in the text (not-in-text terms). The basic assumption we have is that not-in-text terms have a semantic relationship with terms that are in the text. To represent this relationship, we have defined two new features to be represented as input to the classification algorithms. The first feature refers to the power of discrimination of the inferred not-in-text terms. The intuition behind this is that good candidates for a keyphrase are those that are deduced from various textual terms in a specific document and that are not often deduced in other documents. The other feature represents the descriptive strength of a not-in-text candidate. We argue that not-in-text keyphrases must have a strong semantic relationship with the text and that the power of this semantic relationship can be measured in a similar way as popular metrics like TFxIDF. The method proposed in this work was compared with state-of-the-art systems using five corpora and the results show that it has significantly improved automatic keyphrase extraction, dealing with the limitation of extracting keyphrases absent from the text.

Cluster-based Unsupervised Automatic Keyphrases Extraction algorithms: experimentations on Cultural Heritage datasets

Automatic keyword extraction is the process of identifying key terms and key phrases from documents that can appropriately represent the subject of the documents. We present here a work-in-progress, an experimentation done on unsupervised keyword extraction, with the aim of automatically associating scored keyphrases to texts, using (standard or innovative) cluster based methods, and integrating word embedding to enhance semantic relatedness of keyphrases. In the paper we present the datasets used, the state-of-theart for unsupervised automatic extraction algorithms, based on cluster methods, and we describe in details the algorithms implemented and preliminary results obtained. The results obtained are discussed, commented, and compared with those obtained, in previous experimentations, using TextRank, RAKE and Tf-idf.