Text Summarization Techniques Research Articles

News Text Summarization Based on Multi-Feature and Fuzzy Logic

Text Summarization as a phenomenon has always been present and rather an evolving one with the advent of new technologies both in terms of data collection as well for the processing of this data. One reason of using text summarization is the huge amount of data floating over the internet in the form of text files, comments which is though potent enough to be used to extract useful information. but since the amount of text present in these sources is too huge, so the need of text summarization becomes justified by every argument. Some of the areas where text summarization is vastly used is applications involved in providing capsule information such as compact news applications, or websites providing academic notes for various examinations This paper presents an auto text summarizer application which takes the URL of a web page as input, performs summarization on the selected elements and then presents this summarized text content on the front end of a web application. At the backend, the process of scraping of web page content (if an http URL is provided as input) using beautiful soup library or reading of text provided takes place. news in short forms, or micro blogging websites. The scraped content after being preprocessed properly is summarized using a suitable library which in our case is one among NLTK, Spacy, Genism and Sumy. The summarized content is presented at the frontend using flask framework of Python. The results produced using different libraries are compared in the end in terms of reading time of the summarized content. The application uses extractive text summarization technique in order to achieve its result which is a compact summary of the textual data prepared from the keywords already present in the document.

Extractive Text Summarization for Social News using Hybrid Techniques in Opinion Mining

Presently almost all enterprises are oriented into building text data in abundance savoring the benefits of big data concept but the reality is that it’s not practically possible to go through all this data/documents for decision making because of the time constraint. Here in exists intense need of an approach as an alternative for the actual content which can summarize the complete textual content. By adopting these summarizing approaches, the accuracy in data retrieval of summarized content via search queries can be enhanced compared to performing search over the broad range of original textual content. There are many text summarization techniques formulated having their own pros and cons. The present work focuses on a comprehensive news review of extractive text summarization process methods and also taking into account, data appended dynamically. The existing work recommends a technique of hybrid text summarization that’s a blend of CRF (conditional random fields) and LSA (Latent Semantic Analysis) which being highly adhesive with low redundant summary and coherent and in-depth information. The above hybrid techniques is being extracted in five types that being: Positive and negative, statement, questions, suggestions and comments. The technique of LSA extracts hidden semantic structures within words/sentences that being commonly utilized in the process of summarization. The statistical modeling technique of CRF adopts ML (machine leaning) for offering structured detection and providing multiple options for evaluation of opinion summarization thereby identifying the most appropriate algorithm for news text summarizations considering the heavy volume of datasets.

A Formal Technique for Text Summarization from Web Pages by using Latent Semantic Analysis

A Formal Technique for Text Summarization from Web Pages by using Latent Semantic Analysis

Extractive Arabic Text Summarization Using Modified PageRank Algorithm

This paper proposed an approach for Arabic text summarization. Text summarization is one of the natural language processing's applications which is used for reducing the original text amount and retrieving only the important information from the original text. The Arabic language has a complex morphological structure which makes it very difficult to extract nouns to be used as a feature for summarization process. Therefore, Al-Khalil morphological analyzer is used to solve the problem of nouns extraction. The proposed approach is a graph-based system, which represents the document as a graph where the vertices of the graph are the sentences. A Modified PageRank algorithm is applied with an initial score for each node that is the number of nouns in this sentence. More nouns in the sentence mean more information, so nouns count used here as initial rank for the sentence. Edges between sentences are the cosine similarity between the sentences, to get a final summary that contains sentences with more information and well connected with each other. The process of text summarization consists of three major stages: pre-processing stage, features extraction and graph construction stage, and finally applying the Modified PageRank algorithm and summary extraction. The Modified PageRank algorithm used a different number of iterations to find the number returns the best summary results, and the extracted summary depends on compression ratio, taking into account removing redundancy depending on the overlapping between the sentences. To evaluate the performance of this approach EASC Corpus is used as a standard. LexRank and TextRank algorithms were used under the same circumstances, the proposed approach provides better results when compared with other Arabic text summarization techniques. The proposed approach performs efficiently with the number of iteration 10,000.

Techniques of Big Data Text Summarization

In the web industry 4.0, big data are playing a key role in the organizations of their digital transformation journey. Data has also changed intensely in recent years, in volume, variety and velocity. Its fast development attributed to the extensive digitization of business progressions globally. In simple term, data has turn into the new currency of business and its further quick growth will be key transformation and growth of organizations globally. Vast amount of online information, available in healthcare, social media websites, e-commerce web pages, e-books, legal domain, e-news, etc. has made text processing a vital extent of research. The paper starts with introduction about the evolution of web industry 4.0 and digitalization. Followed by the introduction, the paper discusses about big data and text summarization techniques. Further, it describes the literature of text mining that have taken in the recent years. The main objective of this paper is to discuss about the big data text summarization issues and challenges. The paper starts with general introduction of big data and text mining and text summarization. Further it describes recent advances in big data text summarization, and then delve into extraction and abstraction-based text summarization. Finally, the paper concludes with some future research directions.

Abs-Sum-Kan: An Abstractive Text Summarization Technique for an Indian Regional Language by Induction of Tagging Rules

This paper presents a full abstraction for Indian languages, specifically Kannada, in the context of guided summarization. The proposed process generates the abstractive summary by focusing on a unified presentation model with aspect based Information Extraction (IE) rules and scheme based Templates. TF/IDF rules are used for classification into categories. Lexical analysis (like Parts Of Speech tagging and Named Entity Recognition) reduces prolixity, which leads to robust IE rules. Usage of Templates for sentence generation makes the summaries succinct and information intensive. The IE rules are designed to accommodate the complexities of the considered languages. Later, the system aims to produce a guided summary of domain specific documents. An abstraction scheme is a collection of aspects and associated IE rules. Each abstraction scheme is designed based on a theme or subcategory. An extensive statistical and qualitative evaluation of the summaries generated by the system has been conducted and the results are found to be very promising.

Extractive Text Summarization Using Graph Based Ranking Algorithm And Mean Shift Clustering

Text summarization is the process of converting a huge text file into a summarized version preserving its meaning and context. The major aspect of any extractive text summarization technique is to provide precise and accurate summary by using any sentence ranking algorithms. The text is first tokenized and preprocessed. The sentences in the text are ranked according to the corresponding graph based ranking algorithm and finally the top K-scored sentences are included in the summary. Combining the graph based ranking algorithm along with mean shift clustering enables us to efficiently summarize the articles increasing the precision of the system generated summary and also reduces the execution time. The efficiency of these approaches are evaluated using rouge metrics and execution time.

Abs-Sum-Kan: An Abstractive Text Summarization Technique for an Indian Regional Language by Induction of Tagging Rules

This paper presents a full abstraction for Indian languages, specifically Kannada, in the context of guided summarization. The proposed process generates the abstractive sum-mary by focusing on a unified presentation model with aspect based Information Extrac-tion (IE) rules and scheme based Templates. TF/IDF rules are used for classification into categories. Lexical analysis (like Parts Of Speech tagging and Named Entity Recognition) reduces prolixity, which leads to robust IE rules. Usage of Templates for sentence genera-tion makes the summaries succinct and information intensive. The IE rules are designed to accommodate the complexities of the considered languages. Later, the system aims to produce a guided summary of domain specific documents. An abstraction scheme is a collection of aspects and associated IE rules. Each abstraction scheme is designed based on a theme or subcategory. An extensive statistical and qualitative evaluation of the summaries generated by the system has been conducted and the results are found to be very promising.

Passage-Based Text Summarization for Legal Information Retrieval

Automatic text summarization is a process of condensing the content of a text document to pursue the most important information. It plays a significant role in various tasks like text categorization, question answering and information retrieval (IR). As legal information retrieval (LIR) is a subfield of IR, the produced summaries are combined into IR system, with the objective of decreasing the length of the document. In this way, we can improve the access time for searching the information, and relevant documents are retrieved. In this article, we present the creation of passage-level summaries (generic and legal) with different compression ratios and evaluate their performance. The generic summaries present the overall description of the essential information of a document and legal summaries, produced by taking into account the domain-specific features that are present in the document. Next, we propose Boosting Okapi BM25 which is the modified model of Okapi BM25 to increase the efficiency of the LIR. We have evaluated proposed LIR approach in terms of MAP and R-precision and summarization approach using ROUGE tool on FIRE2013 and FIRE2014 datasets. To show the efficacy of the proposed system, we compare the experimental results with different IR models like PL2, \(In\_expB2\), \(In\_expC2\), InL2, \(DFR\_BM25\), Okapi BM25 in terms of MAP. The experimental results of the proposed system show better performance than the existing various IR models in terms of various performance metrics. The empirical results also exhibit that the integration of text summarization and IR techniques helps in retrieving relevant information with less access time.

Self-Tuned Descriptive Document Clustering using a Predictive Network

Document network is defined as a collection of documents that are connected by links. Document clustering become ubiquitous nowadays due to the widespread use of online databases, such as academic search engines. Topic modeling has become a widely used tool for document management because of its superior performance. However, there are few topic models differentiate the importance of documents on different topics. In this survey, can implement text rank algorithms of documents to improve topic modeling and propose to incorporate link based ranking into topic modeling. Text summarization provides an important role in information retrieval. Snippets generated by web search engines for every query result is an application of text summarization. Existing text summarization techniques shows that the indexing is done on the basis of the words present in the document and consists of an array of the posting lists. Document features such as term frequency, text length are used to allocate indexing weight to words. Specifically, topical rank is used to compute the subject stage rating of files, which indicates the significance of documents on special topics. By taking flight the topical ranking of a file as the opportunity of the record concerned in corresponding subject matter, a generalized relation is created between ranking and subject matter modeling. In this thesis, can implement topic discovery model for large number of medical database. The datasets are trained and extract the key terms based text mining and fuzzy latent semantic analysis (FLSA), a novel approach in topic modeling using fuzzy perspective. FLSA can maintain health & medical corpora redundancy problem and provides a new method to estimate the number of topics.

Extractive single document summarization using multi-objective optimization: Exploring self-organized differential evolution, grey wolf optimizer and water cycle algorithm

Text summarization techniques become paramount in extracting relevant information from large databa-ses. Current paper attempts to build some extractive single document text summarization (ESDS) systems using multi-objective optimization (MOO) frameworks. Three techniques are proposed: (1) first is an integration of self-organizing map (SOM) and multi-objective differential evolution (MODE) (named as ESDS_SMODE) (2) second is based on multi-objective grey wolf optimizer (ESDS_MGWO) and (3) third is based on multi-objective water cycle algorithm (ESDS_MWCA). The sentences present in the document are first clustered utilizing the concept of multi-objective clustering. Two objective functions measuring compactness and separation of the sentence clusters in two different ways are optimized simultaneously using MOO framework. The proposed approach is able to automatically detect the number of sentence clusters present in a document and then representative sentences are selected from different clusters using some sentence scoring features to generate the summary. The experiments were conducted on two benchmark datasets, DUC2001, and DUC2002, and the obtained results are compared with various state-of-the-art techniques using ROUGE measures. Results illustrate the superiority of our approach in comparison to state-of-the-art techniques in terms of ROUGE−2 score for both datasets. Code of the developed approach ESDS_SMODE is available online at https://drive.google.com/open?id=1WagTeIDLgphttPrKHpnF_eO7QHWJxXxK.

Improving Extractive Multi-Document Text Summarization Through Multi-Objective Optimization

Multi-document summarization is an optimization problem demanding optimization of more than one objective function simultaneously. The proposed work regards balancing of the two significant objectives: content coverage and diversity when generating summaries from a collection of text documents. Any automatic text summarization system has the challenge of producing high quality summary. Despite the existing efforts on designing and evaluating the performance of many text summarization techniques, their formulations lack the introduction of any model that can give an explicit representation of – coverage and diversity – the two contradictory semantics of any summary . In this work, the design of generic text summarization model based on sentence extraction is redirected into more semantic measure reflecting individually both content coverage and content diversity as two explicit optimization models. The problem is defined by projecting the first criterion, i.e. content coverage in the light of text similarity. The proposed model hypothesizes a possible decomposition of text similarity into three different levels of optimization formula. First, aspire to global optimization, the candidate summary should cover the summary of the document collection. Then, to attain, less global optimization, the sentences of the candidate summary should cover the summary of the document collection. The third level of optimization is content with local optimization, where the difference between the magnitude of terms covered by the candidate summary and those of the document collection should be small. This coverage model is coupled with a proposed diversity model and defined as a Multi-Objective Optimization ( MOO ) problem. Moreover, heuristic perturbation and heuristic local repair operators have been proposed and injected into the adopted evolutionary algorithm to harness its strength. Assessment of the proposed model has been performed using document sets supplied by Document Understanding Conference 2002 ( ) and a comparison has been made with other state-of-the-art methods. Metric used to measure performance of the proposed work is Recall-Oriented Understudy for Gisting Evaluation ( ) toolkit. Results obtained support strong proof for the effectiveness and the significant performance awarded to the proposed MOO model over other state-of-the-art models.

Analysis of Competitor Intelligence in the Era of Big Data: An Integrated System Using Text Summarization Based on Global Optimization

Automatic text summarization can be applied to extract summaries from competitor intelligence (CI) corpora that organizations create by gathering textual data from the Internet. Such a representation of CI text is easier for managers to interpret and use for making decisions. This research investigates design of an integrated system for CI analysis which comprises clustering and automatic text summarization and evaluates quality of extractive summaries generated automatically by various text-summarization techniques based on global optimization. This research is conducted using experimentation and empirical analysis of results. A survey of practicing managers is also carried out to understand the effectiveness of automatically generated summaries from CI perspective. Firstly, it shows that global optimization-based techniques generate good quality extractive summaries for CI analysis from topical clusters created by the clustering step of the integrated system. Secondly, it shows the usefulness of the generated summaries by having them evaluated by practicing managers from CI perspective. Finally, the implication of this research from the point of view of theory and practice is discussed.

Extractive Technique for Text Summarization based on Ranking Scheme

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Enhanced continuous and discrete multi objective particle swarm optimization for text summarization

Reviews from various domains is being posted in web increasingly day by day. Analyzing this enormous content would be useful in decision making for various stakeholders. Text summarization techniques generate concise summaries including sentiments which are useful in analyzing the large content. So text summarization systems become significant in analyzing this huge content. The summaries are generated based on important features using multi objective approaches where sufficient literature is not available. Major limitations of text summarization systems are scalability and performance. Two variants of multi objective optimization techniques such as Discrete and Continuous which work under the principles of particle swarm optimization (PSO) for extractive summarization of reviews had been proposed for performance improvement. The performance is validated using Recall-Oriented Understanding for Gisting Evaluation (ROUGE), Success Counting (SC) and Inverted Generational Distance (IGD). Based on the experimental results it is found that the system is effective using multi-objective PSO algorithm when compared to other state-of-art approaches like Liu’s approach feature based binary particle swarm optimization and etc. for feature based review summarization.

Empirical Analysis of Single and Multi Document Summarization using Clustering Algorithms

The availability of various digital sources has created a demand for text mining mechanisms. Effective summary generation mechanisms are needed in order to utilize relevant information from often overwhelming digital data sources. In this view, this paper conducts a survey of various single as well as multi-document text summarization techniques. It also provides analysis of treating a query sentence as a common one, segmented from documents for text summarization. Experimental results show the degree of effectiveness in text summarization over different clustering algorithms.

Conceptual Demography in Upper Secondary Chemistry and Biology Textbooks’ Descriptions of Protein Synthesis: A Matter of Context?

This study investigates how the domain-specific language of molecular life science is mediated by the comparative contexts of chemistry and biology education. We study upper secondary chemistry and biology textbook sections on protein synthesis to reveal the conceptual demography of concepts central to the communication of this subject. The term “conceptual demography” refers to the frequency, distribution, and internal relationships between technical terms mediating a potential conceptual meaning of a phenomenon. Data were collected through a content analysis approach inspired by text summarization and text mining techniques. Chemistry textbooks were found to present protein synthesis using a mechanistic approach, whereas biology textbooks use a conceptual approach. The chemistry texts make no clear distinction between core terms and peripheral terms but use them equally frequently and give equal attention to all relationships, whereas biology textbooks focus on core terms and mention and relate them to each other more frequently than peripheral terms. Moreover, chemistry textbooks typically segment the text, focusing on a couple of technical terms at a time, whereas biology textbooks focus on overarching structures of the protein synthesis. We argue that it might be fruitful for students to learn protein synthesis from both contexts to build a meaningful understanding.

A Comparative Study of Supervised and Unsupervised Classifiers Utilizing Extractive Text Summarization Techniques to Support Automated Customer Query Question-Answering

A Comparative Study of Supervised and Unsupervised Classifiers Utilizing Extractive Text Summarization Techniques to Support Automated Customer Query Question-Answering

A survey automatic text summarization

Text summarization is compress the source text into a diminished version conserving its information content and overall meaning. Because of the great amount of the information we are provided it and thanks to development of Internet Technologies, text summarization has become an important tool for interpreting text information. Text summarization methods can be classified into extractive and abstractive summarization. An extractive summarization method involves selecting sentences of high rank from the document based on word and sentence features and put them together to generate summary. The importance of the sentences is decided based on statistical and linguistic features of sentences. An abstractive summarization is used to understanding the main concepts in a given document and then expresses those concepts in clear natural language. In this paper, gives comparative study of various text summarization techniques.

Text summarization from legal documents: a survey

Enormous amount of online information, available in legal domain, has made legal text processing an important area of research. In this paper, we attempt to survey different text summarization techniques that have taken place in the recent past. We put special emphasis on the issue of legal text summarization, as it is one of the most important areas in legal domain. We start with general introduction to text summarization, briefly touch the recent advances in single and multi-document summarization, and then delve into extraction based legal text summarization. We discuss different datasets and metrics used in summarization and compare performances of different approaches, first in general and then focused to legal text. we also mention highlights of different summarization techniques. We briefly cover a few software tools used in legal text summarization. We finally conclude with some future research directions.