Keyphrase Extraction Research Articles

Stop-Word Lists in Keyphrase Extraction: Their Influence and Comparison

Stop-Word Lists in Keyphrase Extraction: Their Influence and Comparison

Unsupervised Keyphrase Extraction: Ranking Step and Single-Word Phrase Problem

Unsupervised Keyphrase Extraction: Ranking Step and Single-Word Phrase Problem

HCUKE: A Hierarchical Context-aware approach for Unsupervised Keyphrase Extraction

Keyphrase Extraction (KE) aims to identify a concise set of words or phrases that effectively summarizes the core ideas of a document. Recent embedding-based models have achieved state-of-the-art performance by jointly modeling local and global contexts in Unsupervised Keyphrase Extraction (UKE). However, these models often ignore either sentence- or document-level contexts, leading directly to weak or incorrect global significance. Furthermore, they rely heavily on local significance, making them vulnerable to noisy data, particularly in long documents, resulting in unstable and suboptimal performance. Intuitively, hierarchical contexts enable a more accurate understanding of the candidates, thereby enhancing their global relevance. Inspired by this, we propose a novel Hierarchical Context-aware Unsupervised Keyphrase Extraction method called HCUKE. Specifically, HCUKE comprises three core modules: (i) a hierarchical context-based global significance measure module that incrementally learns global semantic information from a three-level hierarchical structure; (ii) a phrase-level local significance measure module that captures local semantic information by modeling the context interaction among candidates; and (iii) a candidate ranking module that integrates the measure scores with positional weights to compute a final ranking score. Extensive experiments on three benchmark datasets demonstrate that the proposed method significantly outperforms state-of-the-art baselines.

Comparative Analysis on Automatic Keyphrase Extraction (AKPE) Techniques

Abstract— Extracting Keyphrase from the large corpus manually is a tedious task. To overcome this challenging task Automatic Keyphrase Extraction (AKPE) techniques have been used for Keyphrase extraction. Keyphrase extraction is the task of automatically selecting a small set of phrases that best describe a given free text document. Keyphrase constitutes 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 paper contains a brief description of automatic Keyphrase extraction techniques which consist of various algorithms and also a comparative study on automotive Keyphrase extraction techniques such as KEA, TF-IFD, RAKE, TextRank. Keywords— Keyphrase Extraction, Techniques, Comparison, Keyphrase Extraction Algorithm, Term Frequency- Inverse Frequency Document, Rapid Automatic Keyphrase Extraction, TextRank.

A method of identifying domain-specific academic user information needs based on academic Q&A communities

PurposeTo address the shortcomings of existing academic user information needs identification methods, such as low efficiency and high subjectivity, this study aims to propose an automated method of identifying online academic user information needs.Design/methodology/approachThis study’s method consists of two main parts: the first is the automatic classification of academic user information needs based on the bidirectional encoder representations from transformers (BERT) model. The second is the key content extraction of academic user information needs based on the improved MDERank key phrase extraction (KPE) algorithm. Finally, the applicability and effectiveness of the method are verified by an example of identifying the information needs of academic users in the field of materials science.FindingsExperimental results show that the BERT-based information needs classification model achieved the highest weighted average F1 score of 91.61%. The improved MDERank KPE algorithm achieves the highest F1 score of 61%. The empirical analysis results reveal that the information needs of the categories “methods,” “experimental phenomena” and “experimental materials” are relatively high in the materials science field.Originality/valueThis study provides a solution for automated identification of academic user information needs. It helps online academic resource platforms to better understand their users’ information needs, which in turn facilitates the platform’s academic resource organization and services.

Extracting Key-phrase Embedding using Deep Average Network and Maximal Marginal Relevance to Enhance Information Retrieval

Background: Automatic keyphrase extraction (AKE) is essential to many NLP and information retrieval tasks. Extracting high-quality key phrases is difficult due to technological advancements and the exponential growth ‎of ‎textual data and digital sources. Unsupervised keyphrase extraction with cheap computing cost that relies ‎on ‎heuristic notions of phrase importance such as embedding similarities but their development necessitates in-depth subject expertise. Materials and Methods: This paper presents a method to obtain a semantic understanding of the query ‎and index documents by using ‎the embedding technique(universal Sentence ‎encoder (USE) ) while keeping the most informative ‎using Maximal Marginal Relevance (MMR) and then scoring(an ‎inverted index) the most documents relevant to the query ‎vector to improve the ‎performance of IR systems.‎ Results: The proposed retrieval model implement on the (Fire2011) dataset. The final ‎stage was evaluating the results of the baseline and the results (indexing and ranking) by using mean average precision (MAP). The ‎result of the baseline was 0.61, while the result ‎inverted index was 0.6277519 .‎ Conclusions: In this paper, we have discussed document retrieval using keep key phrases that ‎have ‏informativeness ‎properties by ‎using maximal ‎marginal ‎relevance, since if we extract a fixed number of top ‎keyphrases, ‎redundancy ‎hinders the diversification of the ‎extracted keyphrases.

Enhancing unsupervised keyphrase extraction through the integration of structural details in embedding-based approaches

Enhancing unsupervised keyphrase extraction through the integration of structural details in embedding-based approaches

Keyphrase Extraction from Scientific Articles

Keyphrase extraction is a crucial task in natural language processing (NLP) that involves identifying important terms and phrases in a text. This paper presents a methodology for extracting keyphrases from scientific articles using a combination of preprocessing techniques and the term frequency-inverse document frequency (TF-IDF) algorithm. The approach includes tokenization, stopword removal, and punctuation elimination, followed by the application of the TF-IDF vectorizer to identify and score keyphrases. The results demonstrate the effectiveness of the method in highlighting significant terms in scientific texts.

Comparative Evaluation of Keyphrase Extraction Tools for Semantic Analysis of Climate Change Scientific Reports and Ontology Enrichment

Comparative Evaluation of Keyphrase Extraction Tools for Semantic Analysis of Climate Change Scientific Reports and Ontology Enrichment

Keyphrase extraction using graph-based statistical approach with NLP patterns

Keyphrase extraction using graph-based statistical approach with NLP patterns

A Systematic Review of Research on Gender Diversity in STEM Education

Science, technology, engineering, and mathematics (STEM) education research has increased worldwide in recent years. Recognition of STEM education's importance in producing highly skilled scientists and technologists and its economic benefits to society are driving this increase. However, STEM labor force gender representation is a persistent issue. A meta-analysis using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol and a thorough literature search assessed STEM gender diversity. A total of 42 STEM education gender diversity research papers were reviewed using PRISMA. These Scopus papers provide a database from 2013 to 2023. The study used text analysis and synthesis to evaluate and highlight the countries, extract key phrases, identify research issues of interest to scholars, and suggest future research directions. The research findings indicate that current publications primarily focus on developing STEM education programs to attract and provide more opportunities for women. Additionally, other research areas of interest include proposing solutions to enhance gender equality in STEM education, women's awareness, and skills in the STEM field. Furthermore, research gaps have been identified in course design, learning material development for STEM education, and establishing a direction for implementing STEM education starting from early elementary school, with the optimal period being before grade 3. These findings can help academics explore STEM research ideas in new situations. However, the investigation was limited by using only a Scopus catalog database; disregarding alternative sources may have led to material being omitted.

Developing a hierarchical model for unraveling conspiracy theories

A conspiracy theory (CT) suggests covert groups or powerful individuals secretly manipulate events. Not knowing about existing conspiracy theories could make one more likely to believe them, so this work aims to compile a list of CTs shaped as a tree that is as comprehensive as possible. We began with a manually curated ‘tree’ of CTs from academic papers and Wikipedia. Next, we examined 1769 CT-related articles from four fact-checking websites, focusing on their core content, and used a technique called Keyphrase Extraction to label the documents. This process yielded 769 identified conspiracies, each assigned a label and a family name. The second goal of this project was to detect whether an article is a conspiracy theory, so we built a binary classifier with our labeled dataset. This model uses a transformer-based machine learning technique and is pre-trained on a large corpus called RoBERTa, resulting in an F1 score of 87%. This model helps to identify potential conspiracy theories in new articles. We used a combination of clustering (HDBSCAN) and a dimension reduction technique (UMAP) to assign a label from the tree to these new articles detected as conspiracy theories. We then labeled these groups accordingly to help us match them to the tree. These can lead us to detect new conspiracy theories and expand the tree using computational methods. We successfully generated a tree of conspiracy theories and built a pipeline to detect and categorize conspiracy theories within any text corpora. This pipeline gives us valuable insights through any databases formatted as text.

AdaptiveUKE: Towards adaptive unsupervised keyphrase extraction with gated topic modeling

Keyphrase extraction aims to identify the purport-specific words or phrases from a free-format natural language document, which also requires the keyphrases to be concise and informative. Existing works mainly encode the document and phrases into the same vector space and score keyphrases directly employing similarity measures. However, such methods have two major limitations: (1) The semantic richness of documents in real-world scenarios can be diverse and it is tricky to model such diversity when representing a document with one specific embedding. (2) Using similarity measures alone to score keyphrases fails to detect phrases from all related topics, leading to semantics over-concentration of extracted results. To address these challenges, we propose AdaptiveUKE,22Our code is released at https://github.com/NLPCodebase/AdaptiveUKE. a simple yet effective model for adaptive unsupervised keyphrase extraction. Firstly, to adapt to the varying degree of document semantic richness, we propose a novel gated topic modeling strategy, allowing each topic to be assigned independently rather than in a competitive fashion. Secondly, we design a topic-guided scoring algorithm to extract keyphrases under unsupervised conditions. Fusing the importance and relatedness of each topic enables efficient ranking of candidate phrases. Finally, we conduct extensive experiments on six widely-used datasets. The results show that AdaptiveUKE achieves highly competitive results across several keyphrase extraction benchmarks, surpassing state-of-the-art on Inspec, DUC2001, SemEval2010 and reaching the current best performance on SemEval2017, Theses100, Krapivin2009.

A Contrastive Learning Framework for Keyphrase Extraction

A Contrastive Learning Framework for Keyphrase Extraction

MICRank: Multi-information interconstrained keyphrase extraction

Keyphrase Extraction is an automatic task that involves identifying the key words or phrases that capture the main content of an article. It is useful for various downstream tasks, including text search, text clustering, and text classification. Embedding-based methods for keyphrase extraction have shown promising results by utilizing pre-trained language models to represent candidate phrases and documents separately. These methods then rank the candidate phrases based on the cosine similarity between the document and the candidate phrases embeddings. However, there are mainly two shortcomings in such methods: I) Redundancy errors, when there are partial repetitions of candidate keyphrases, the methods tend to use redundant long phrases as keyphrases; II) Low keyphrase coverage, such as some keyphrases used to describe locally important information are ignored. In this paper, we propose an unsupervised keyphrase extraction method called “MICRank”, which evaluates the importance of candidate keyphrases from three perspectives: global information, local information, and attribute information, and solved the aforementioned issues. The experimental results on six benchmarks demonstrate that the proposed MICRank method outperforms the state-of-the-art unsupervised keyphrase extraction methods. In addition, this paper improves the judgment criterion of correct keyphrase extraction and introduces a new evaluation metric called S1@M (M ∈ {5,10,15}) to address the issue of synonyms being considered incorrect predictions.

Y-Rank: A Multi-Feature-Based Keyphrase Extraction Method for Short Text

Keyphrase extraction is a critical task in text information retrieval, which traditionally employs both supervised and unsupervised approaches. Supervised methods generally rely on large corpora, which introduce the problems of availability, while unsupervised methods are independent of out-sources but also lead to defects like imperfect statistical features or low accuracy. Particularly in short-text scenarios, limited text features often result in low-quality candidate ranking. To address this issue, this paper proposes Y-Rank, a lightweight unsupervised keyphrase extraction method that extracts the average information content of candidate sentences as the key statistical features from a single document, and follows a graph construction approach based on similarity to obtain the semantic features of keyphrase with high-quality and ranking accuracy. Finally, the top-ranked keyphrases are acquired by the fusion of these features. The experimental results on five datasets illustrate that Y-Rank outperforms the other nine unsupervised methods, achieves enhancements on six accuracy metrics, including Precision, Recall, F-Measure, MRR, MAP, and Bpref, and performs the highest improvement in short text scenarios.

Keyphrase Extraction Using TextRank for Indonesian Text

Keywords are commonly used as a form of summary from scientific publications. But in determining keywords, it requires expertise in the related field and a long amount of time because you have to read and understand the entire contents of scientific publications. Keyphrase Extraction can be a solution to get relevant keywords in a short time based on titles and abstracts from scientific publications. TextRank method is used to extract keywords. This research will perform keyword extraction using the TextRank method for Indonesian text. The evaluation results of this study showed an accuracy value of 95.53% and an f1-score of 59.32% with a threshold configuration of 80% and using all keyword candidates.

PhraseMap: Attention-Based Keyphrases Recommendation for Information Seeking.

Many Information Retrieval (IR) approaches have been proposed to extract relevant information from a large corpus. Among these methods, phrase-based retrieval methods have been proven to capture more concrete and concise information than word-based and paragraph-based methods. However, due to the complex relationship among phrases and a lack of proper visual guidance, achieving user-driven interactive information-seeking and retrieval remains challenging. In this study, we present a visual analytic approach for users to seek information from an extensive collection of documents efficiently. The main component of our approach is a PhraseMap, where nodes and edges represent the extracted keyphrases and their relationships, respectively, from a large corpus. To build the PhraseMap, we extract keyphrases from each document and link the phrases according to word attention determined using modern language models, i.e., BERT. As can be imagined, the graph is complex due to the extensive volume of information and the massive amount of relationships. Therefore, we develop a navigation algorithm to facilitate information seeking. It includes (1) a question-answering (QA) model to identify phrases related to users' queries and (2) updating relevant phrases based on users' feedback. To better present the PhraseMap, we introduce a resource-controlled self-organizing map (RC-SOM) to evenly and regularly display phrases on grid cells while expecting phrases with similar semantics to stay close in the visualization. To evaluate our approach, we conducted case studies with three domain experts in diverse literature. The results and feedback demonstrate its effectiveness, usability, and intelligence.

Hybrid Approach To Unsupervised Keyphrase Extraction

The exponential growth of textual data poses a monumental challenge for extracting meaningful knowledge. Manually identifying descriptive keywords or keyphrases for each document is infeasible given the massive daily generated text. Automatic keyphrase extraction is, therefore, essential. However, current techniques struggle with learning the most salient semantic features from lengthy documents. This hybrid keyphrase extraction framework uniquely combines the complementary strengths of graph-based and textual feature methods. Our approach demonstrates improved performance over relying solely on statistical or graphical. Graph-based systems leverage word co- occurrence networks to score importance. Textual methods extract keyphrases using linguistic properties. Together, these complementary techniques overcome the limitations of relying on any strategy. The hybrid approach is evaluated on standard SemEval 2017 Task 10 and SemEval 2010 Task 5 benchmark datasets for scientific paper keyphrase extraction. Performance is quantified using the F1 score relative to human-annotated ground truth keyphrase. Results will quantify effectiveness on long documents with thousands of terms where only a few keywords represent salient concepts. Results show our technique effectively identifies the most salient semantic keywords, overcoming limitations of current techniques that struggle to mix features of graphical or statistical methods. Our experiments demonstrate that the proposed hybrid approach achieves superior F1 scores compared to current state-of-the-art methods on benchmark datasets. These results validate that synergistically combining graph and textual features enables more accurate keyphrase extraction, especially for long documents laden with extraneous terms.

Key phrase extraction from patient’s chief complaints

Over the past ten years, there have been notable advancements in natural language processing (NLP) systems. The goal of the study is to extract relevant key phrases from the Marathi patient major complaints. Our data came from a single, non-profit private hospital located in a remote part of North Maharashtra Region, India. From the hospital's information around 5000 patient visits occurred between 2021 and 2022.A total of 54000 text-free chief complaints were gathered as a result of the initiatives. This paper summarizes a study that focuses on pre-processing strategies that were established in an efficient way to extract pertinent keywords from phrases in accordance with the patients' primary concerns. The methods and findings from this experiment are presented in this paper.