TextRank Algorithm Research Articles

Summarization of Software Bug Report based on Sentence Semantic Similarity (SSBRSSS) Technique

In this work, sentence similarity between sentences of software bug report is computed. For this, two methods are utilized, Latent Semantic Analysis and Text Rank. Latent Semantic Analysis is used to compute semantic similarity between sentences of bug reports which infers deeper and hidden relation between words. From this, a pair of sentences with semantic similarity above a set threshold is selected and from one pair of sentences, only one sentence is selected. The remaining sentences are passed into TextRank algorithm and sentences with high similarity are further selected to generate a coherent summary. The proposed approach is evaluated on a newly constructed Apache Project Bug Report Corpus and existing Bug Report Corpus. The proposed approach is also compared with baseline approaches that mainly focus on only lexical similarity. The results when evaluated on Apache project Bug Report Corpus attains an average value of 80%, 72.57%, 76.05% and 76.57% in terms of precision, recall, F-score and pyramid precision respectively.

Innovative Cultural Tourism in the Perspective of Cultural Ecology in the Context of Big Data: Theory, Strategy, Practice

Abstract Taking cultural ecology as the carrier of tourism resources, it fully embodies the significance of the synergistic development of cultural tourism ecology for the innovation of the cultural tourism model. The article establishes a cultural tourism ecological synergy model by integrating cultural ecology and cultural tourism under the guidance of the theory of the life cycle of tourist places and industrial innovation. Taking the nine provinces of the Yellow River as the research object, its cultural tourism data are collected and pre-processed, cultural tourism keywords are extracted by the TextRank algorithm, and the LDA model is established to obtain cultural tourism themes. The Skip-gram model was combined with the GCN network to establish a cultural tourism emotional image perception model, and the Moran index and nearest-neighbor index were introduced to analyze the spatial structure and regional evolution of cultural tourism attractions. The keywords of cultural tourism are mainly embodied in ecology and culture, which all appear more than 1,000 times, and the percentage of tourists’ most concerned cultural tourism themes is 22.15%, and most of the nearest neighbor indices of the spatial structure of the attractions at different levels are less than 1, which is cohesive. The Moran index of tourists’ footprints in 2022 is 0.106, the footprint coverage rate is 0.26 percentage points higher than that in 2012, and tourists’ positive emotional evaluation of cultural tourism destinations reaches 80%. Based on cultural ecology, realizing the synergistic development of cultural tourism and ecology is the way to innovate cultural tourism.

Research on Optimizing English Translation Teaching Methods for College Students Using Machine Learning Technology

Abstract With the changes in the market situation for English majors, teaching English translation in colleges and universities is also facing many challenges. This paper proposes an optimization strategy for English translation teaching methods by using machine learning technology to automatically identify English translation errors and extract text summaries. Pearson coefficient and multi-feature fusion technology are used to prejudge the correctness of English translation results, and according to the directed graph of wrong translation results, the automatic identification algorithm of English translation errors is constructed to automatically identify translation errors. The unsupervised machine learning TextRank algorithm is introduced and applied in text summary extraction, and combined with a multi-feature fusion computer system based on similarity relationships, it is improved to enhance the efficiency and quality of text extraction. Inner Mongolia Normal University set up an experimental class and a control class and applied this paper’s technology to practice English translation teaching. After the practice, the total English translation score of students in the experimental class was 85.74, which was 4.41 higher than that of the control group, showing a significant difference (P<0.05). Prior to the practice, the interest and attitude toward English translation rose from 3.42 and 2.43 to 4.32 and 4.75, while the control group’s mean values decelerated slightly. The two dimensions of teaching satisfaction, learning atmosphere, and English translation ability development were also higher than the control class by 0.71 and 0.91, indicating a statistical difference (P<0.05).

National Image Construction Based on Multimodal Translation in the Context of Big Data

Abstract This study focuses on the public’s perception of a specific country’s image, and adopts comment texts from multimodal translated materials as the object of analysis. A country image text perception model was established by combining the TextRank algorithm and the LDA topic model. The model extracts and categorizes themes from the collected comment data, and discusses in depth the different themes of country image and their degree of attention. It is found that the most popular theme is “economic dimension”, with a heat degree of 42.59%, followed by “regional development” and “political dimension”. This reflects the high level of public interest in these areas. In addition, the study quantified the public’s emotional disposition towards the country’s image and found that most people have a positive attitude towards the country, with a high percentage of positive sentiment of 84.89%, but most of them are mildly positive. This suggests that although the public’s overall perception tends to be positive, there is room to improve the country’s image and enhance the public’s positive perception.

From Posts to Personas: Classifying Users in the Social Media Landscape

Social media is a digital environment where users openly share their opinions and engage in debates and discussions on various topics. Social media has amassed an enormous quantity of accessible data due to its constantly expanding and highly active user base. This data is a valuable resource for researching a variety of topics. An example of such a research problem is identifying user similarities by analyzing their data. This article explores combining comments, textual posts, and likes for comments to create keywords or tags and group the users. These tags are extracted from user data and utilized in constructing a tag network. The tag network facilitates the formation of communities consisting of users with similar interests. User grouping is achieved based on the tags extracted from the posts. The proposed methods employ TF-IDF (term frequency-inverse document frequency) and TextRank algorithms to extract the tags. Kernel diffusion determines the similarities between tags in the tag network. Finally, an aggregation-hierarchical clustering algorithm is employed to group social media users based on these tags.

Criminal law regime for electronic money crimes in the context of the digital economy

Abstract E-money is a product of the fusion and development of network technology and digital economy, which has the attributes of data, property and money, and the current Criminal Law’s regulatory response to e-money crimes has highlighted the inadequacy. In order to improve the criminal law regulation of e-money crimes, this paper combines the Siamese-BiLSTM model based on the attention mechanism and TextRank algorithm to design a similar case retrieval model for e-money crimes, in order to analyze the loopholes of the criminal law regulation and the reasons for their emergence, and thus to give the path suggestions. Compared with the baseline model Siamese-BiLSTM, the accuracy and F1 value of the improved model in this paper are improved by 5.09% and 5.20% on average, respectively, and the removal of any module leads to a decrease in model performance. This indicates that the improvement of the e-money crime similar case retrieval model based on SBA+TextRank in this paper is better, and it is applicable to the similarity calculation of legal case texts. This paper provides a feasible path for improving the criminal law system for e-money crimes.

A Study of Innovative Entrepreneurial Behavior of College Students under Algorithmic Recommendation

Abstract In this paper, for a large amount of text content such as college students’ innovation and entrepreneurship education practice, considering the problem of directionality, based on the idea of a recommendation algorithm, we use word similarity and TextRank algorithm to extract the keywords of innovation and entrepreneurship behaviors as the first-knowledge labels of the textual resources, and introduce complex labeling network based on this. On the premise of semantic-based TextRank keywords and tag network, the fusion-gated graph attention group recommendation algorithm is used to construct college students’ innovation and entrepreneurship practice platform, and the simulation analysis of college students’ innovation and entrepreneurship practice platform is carried out. The results show that the survival rate of virtual startups can be calculated as 5.37% by using the college students’ innovation and entrepreneurship practice platform, which can accurately analyze that college students’ innovation and entrepreneurship behaviors are stronger, but the survival rate of the enterprises founded by college students for the first time needs to be improved. This study can provide theoretical knowledge guidance for the study of college students’ innovative entrepreneurial behavior and has a facilitating effect on the development of innovation and entrepreneurship education in colleges and universities.

Product improvement in a big data environment: A novel method based on text mining and large group decision making

Product improvement has become a multifaceted and uncertain endeavour for manufacturers in an increasingly competitive business environment. Online platforms have emerged to solicit consumer opinions and product feedback. However, product improvement requires a critical yet complex decision-making approach for manufacturers. Managers face the challenge of identifying the most effective decision-making methodology for product improvement, especially in a big data environment. In this research, we comprehensively evaluate different product improvement decision-making methodologies through a series of experimental investigations. Specifically, three different experiments are conducted, including: i) an initial selection guided by intuitive perception, ii) expert decision-making, and iii) a hybrid method that incorporates consumer big data and large-scale group decision-making. Product criteria sets are categorized using the Latent Dirichlet Allocation (LDA) method, while the importance of these criteria is determined by applying the TextRank and Word2Vec algorithms. Our empirical results show that the mixed method, which utilizes text-mining techniques in conjunction with large-group decision-making, provides a more reliable and effective approach to facilitating product improvement.

ERNIE and Multi-Feature Fusion for News Topic Classification

Traditional news topic classification methods suffer from inaccurate text semantics, sparse text features and low classification accuracy. Based on this, this paper proposes a news topic classification method based on Enhanced Language Representation with Informative Entities (ERNIE) and multi-feature fusion. A semantically more accurate representation of text embedding is obtained by ERNIE. In addition, this paper extracts word, context and key sentence based on the news text. The key sentences of the news are obtained through the TextRank algorithm, which enables the model to focus on the content points of the news. Finally, this paper uses the attention mechanism to realize the fusion of multiple features. The proposed method is experimented on BBCNews. The experimental results show that we achieve classification accuracies superior to those of the compared methods, while validating the structural validity of the proposed method. The method in this paper has a positive effect on promoting the research of news topic classification.

Enhanced TextRank using weighted word embedding for text summarization

<p><span lang="EN-US">The length of a news article may influence people’s interest to read the article. In this case, text summarization can help to create a shorter representative version of an article to reduce people’s read time. This paper proposes to use weighted word embedding based on Word2Vec, FastText, and bidirectional encoder representations from transformers (BERT) models to enhance the TextRank summarization algorithm. The use of weighted word embedding is aimed to create better sentence representation, in order to produce more accurate summaries. The results show that using (unweighted) word embedding significantly improves the performance of the TextRank algorithm, with the best performance gained by the summarization system using BERT word embedding. When each word embedding is weighed using term frequency-inverse document frequency (TF-IDF), the performance for all systems using unweighted word embedding further significantly improve, with the biggest improvement achieved by the systems using Word2Vec (with 6.80% to 12.92% increase) and FastText (with 7.04% to 12.78% increase). Overall, our systems using weighted word embedding can outperform the TextRank method by up to 17.33% in ROUGE-1 and 30.01% in ROUGE-2. This demonstrates the effectiveness of weighted word embedding in the TextRank algorithm for text summarization.</span></p>

Extractive Summarization of Telugu Text Using Modified Text Rank and Maximum Marginal Relevance

With the rapid growth of digital content, there is a need for an automatic text summarizer to provide short text from a long text document. Many research works have been presented for extractive text summarization (ETS). This article mainly focuses on the graph-based ETS approach for multiple Telugu text documents. A modified Text-Rank algorithm is employed with the noun and verb count of each sentence in the text as the initial score of each node. To get the optimal features, a novel feature selection algorithm called improved Flamingo Search Algorithm is proposed in this article. Though graph-based ETS is an important approach, the generated summaries are redundant. To reduce the redundancy in the generated summary, maximum marginal relevance is combined with the modified Text-Rank. Different word-embedding techniques such as Fast-Text, Word2vec, TF-IDF, and one-hot encoding are utilized to experiment with the proposed approach. The performance of the proposed text summarization approach is evaluated with BLEU and ROUGE in terms of F-measure, precision, and recall.

Identification of actual energy policy and security research topics in Elsevier and MDPI publications for 2021–2023. Part 2. Clustering of publications. Automatic extractive referencing

This article focuses on identifying relevant research topics in Elsevier and MDPI publications 2021–2023 on energy policy and security based on publication clustering. It is shown that name groups extracted from titles and abstracts can be used to cluster publications to identify relevant research topics. The extraction of noun phrases was carried out using the Sifaka software package. A table of the 5,000 most frequent name groups was constructed, the terms of which were retained in the title and abstract texts, and the remaining terms were deleted. The prepared texts were used to cluster the bibliometric records using a demo version of Carrot2 and the Lingo3G algorithm. The annotation texts for each cluster were subjected to automatic extractive referencing using the sumy program with the Text-rank algorithm and the stopwords-en-ISO.txt list. The possibility of using extractive referencing technology to describe the dominant topic of individual publication clusters is demonstrated. A list of dominant themes in energy policy and security publications in Elsevier and MDPI publications for 2021–2023 is given.

A Study on the Strategic Application of Semantic Segmentation Based on Improved Convolutional Neural Networks on English-Chinese Interaction

Teachers need to provide numerous examples sentences for students to translate in the process of teaching English, but the number of sentences given by teachers to practice subjectively is not enough. Therefore, the study constructs a text generation model using an improved convolutional neural network semantic segmentation method, where the corpus utterances are keyword extracted and new shorter utterances are generated based on the keywords for language learners to practice translation. The research first uses the textRank algorithm to extract semantic keywords to obtain a dataset, and then uses CNN to construct an encoder to achieve semantic encoding of the keyword dataset. However, during the research process, it was found that traditional CNN models are relatively sensitive to the location of input data. Therefore, the research introduces the idea of Decomposition Machine (FM) to improve the encoder. In order to control text generation, research has introduced a weighted additive attention mechanism in the decoding process to associate the meaning of the generated text sequence with the meaning of the keyword set. Based on this, a text generation model for generating a translation related corpus in English teaching is constructed. This results in a text generation model that can be used to generate a translation-linked corpus for English language teaching. The average BLEU value of model 1 is 0.924, Inform value is 98.40, the Success value is 97.64, and the Combine value is 101.24, which can achieve high-quality text generation by the keyword lexical meaning and provide technical guarantee for the establishment of the corpus in educating in English.

Text Summarization Using Natural Language Processing

The availability of information today accessible in digital form has accelerated. Retrieving useful document from such large pool of information gets difficult. So, to summarize these text documents is very crucial. Text summarization is a process of minimizing the original source document to get essential information of that document. It eliminates the redundant, less important content and provides you with the vital information in a shorter version usually half a length of the original text. Creating a manual summary is a very time-consuming task. Automatic summarization helps in getting the gist of information present in a particular document in a very short period. In the comparison of all Indian regional languages, there is very less amount of work done for summarization of Hindi documents. This paper presents an effective way to summarize using a Text Rank algorithm. It focuses on summarizing single Hindi text document at a time based on natural language processing (NLP).

Extractive Article Summarization Using Integrated TextRank and BM25+ Algorithm

The quantity of textual data on the internet is growing exponentially, and it is very tough task to obtain important and relevant information from it. An efficient and effective method is required that provides a concise summary of an article. This can be achieved by the usage of automatic text summarization. In this research, the authors suggested an efficient approach for text summarization where an extractive summary is generated from an article. The methodology was modified by integrating a normalized similarity matrix of both BM25+ and conventional TextRank algorithm, which resulted in the improvised results. A graph is generated by taking the sentences in the article as nodes and edge weights as the similarity score between two sentences. The maximum rank nodes are selected, and the summary is extracted. Empirical evaluation of the proposed methodology was analyzed and compared with baseline methods viz. the conventional TextRank algorithm, term frequency–inverse document frequency (TF–IDF) cosine, longest common consequence (LCS), and BM25+ by taking precision, recall, and F1 score as evaluation criteria. ROUGE-1, ROUGE-2, and ROUGE-L scores were calculated for all the methods. The outcomes demonstrate that the proposed method can efficiently summarize any article irrespective of the category it belongs to.

A hybrid algorithm for clinical decision support in precision medicine based on machine learning

PurposeThe objective of the manuscript is to propose a hybrid algorithm combining the improved BM25 algorithm, k-means clustering, and BioBert model to better determine biomedical articles utilizing the PubMed database so, the number of retrieved biomedical articles whose content contains much similar information regarding a query of a specific disease could grow larger.Design/methodology/approachIn the paper, a two-stage information retrieval method is proposed to conduct an improved Text-Rank algorithm. The first stage consists of employing the improved BM25 algorithm to assign scores to biomedical articles in the database and identify the 1000 publications with the highest scores. The second stage is composed of employing a method called a cluster-based abstract extraction to reduce the number of article abstracts to match the input constraints of the BioBert model, and then the BioBert-based document similarity matching method is utilized to obtain the most similar search outcomes between the document and the retrieved morphemes. To realize reproducibility, the written code is made available on https://github.com/zzc1991/TREC_Precision_Medicine_Track.FindingsThe experimental study is conducted based on the data sets of TREC2017 and TREC2018 to train the proposed model and the data of TREC2019 is used as a validation set confirming the effectiveness and practicability of the proposed algorithm that would be implemented for clinical decision support in precision medicine with a generalizability feature.Originality/valueThis research integrates multiple machine learning and text processing methods to devise a hybrid method applicable to domains of specific medical literature retrieval. The proposed algorithm provides a 3% increase of P@10 than that of the state-of-the-art algorithm in TREC 2019.

Application of Text Rank Algorithm Fused With LDA in Information Extraction Model

Application of Text Rank Algorithm Fused With LDA in Information Extraction Model

TG-SMR: A Text Summarization Algorithm Based on Topic and Graph Models

Recently, automation is considered vital in most fields since computing methods have a significant role in facilitating work such as automatic text summarization. However, most of the computing methods that are used in real systems are based on graph models, which are characterized by their simplicity and stability. Thus, this paper proposes an improved extractive text summarization algorithm based on both topic and graph models. The methodology of this work consists of two stages. First, the well-known TextRank algorithm is analyzed and its shortcomings are investigated. Then, an improved method is proposed with a new computational model of sentence weights. The experimental results were carried out on standard DUC2004 and DUC2006 datasets and compared to four text summarization methods. Finally, through experiments on the DUC2004 and DUC2006 datasets, our proposed improved graph model algorithm TG-SMR (Topic Graph-Summarizer) is compared to other text summarization systems. The experimental results prove that the proposed TG-SMR algorithm achieves higher ROUGE scores. It is foreseen that the TG-SMR algorithm will open a new horizon that concerns the performance of ROUGE evaluation indicators.

Data Mining in Coal-Mine Gas Explosion Accidents Based on Evidence-Based Safety: A Case Study in China

From an informatics perspective, decision-making failures in accident prevention are due to insufficient necessary safety evidence. Analyzing accident data can help in obtaining safety evidence. Currently, such a practice mostly relies on experts’ judgement and experience, which are subjective and inefficient. Furthermore, due to the inadequate safety-related theoretical support, the sustainable safety of a system can hardly be achieved purposefully. To automatically explore and obtain latent safety evidence in coal-mine data, and improve the reliability and sustainability of coal-mine safety management, a novel framework of combining data mining technology and evidence-based safety (EBS) theory is proposed, and was applied to a coal gas explosion accident. First, the term frequency-inverse document (TF-IDF) and TextRank algorithms were fused to extract keywords, and keyword evolution word cloud maps from the time dimension were drawn to obtain keyword safety evidence. Then, on the basis of the latent Dirichlet allocation (LDA) model, the best safety evidence, such as accident causation topics and causation factors, were mined, and safety decisions were given. The results show that accident data mining, based on evidence-based safety, can effectively and purposefully mine the best safety evidence, and guide safety decision making to optimize safety management models and achieve sustainable safety.

Extractive Summarization on Food Reviews

Text summarization is a technique to summarize the content of a sizeable text but meanwhile it keep the key information. Extractive summarization and abstractive summarization are the main techniques for text summarization. TextRank algorithm, an extractive summarization technique is applied to perform automatic text summarization in this study. Furthermore, GloVe pre-trained word embedding model is used to map each word from the reviews to vector representation. In the end, PageRank algorithm is applied to rank the sentences based on their sentence ranking scores. The more important and relevant sentences which can be the representatives of summary will be placed in a higher rank. The objective of our study is to extract top five reviews with the highest sentence ranking scores which can form a summary to provide a conspectus of a cookies brand in Amazon food reviews. An analysis of the customer perception based on the summary generated is conducted to understand their needs and level of satisfactions. The final summary demonstrates that Amazon customer reviews for certain cookies brand are generally positive.