A Newly Proposed Technique for Summarizing the Abstractive Newspapers’ Articles based on Deep Learning

Jan 6, 20210 likes33 views

In this new era, where tremendous information is available on the internet, it is of most important to provide the improved mechanism to extract the information quickly and most efficiently. It is very difficult for human beings to manually extract the summary of large documents of text. Therefore, there is a problem of searching for relevant documents from the number of documents available, and absorbing relevant information from it. In order to solve the above two problems, the automatic text summarization is very much necessary. Text summarization is the process of identifying the most important meaningful information in a document or set of related documents and compressing them into a shorter version preserving its overall meanings. More specific, Abstractive Text Summarization (ATS), is the task of constructing summary sentences by merging facts from different source sentences and condensing them into a shorter representation while preserving information content and overall meaning. This Paper introduces a newly proposed technique for Summarizing the abstractive newspapers’ articles based on deep learning.

A Newly Proposed Technique for Summarizing the Abstractive Newspapers’ Articles based on Deep Learning

This document summarizes an article that proposes an automatic text summarization technique using feature terms to calculate sentence relevance. The technique uses both statistical and linguistic methods to identify semantically important sentences for creating a generic summary. It determines the relevance of sentences based on feature term ranks and performs semantic analysis of sentences with the highest ranks to select those most important for the summary. The performance is evaluated by comparing summaries to those created by human evaluators.

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Complete agglomerative hierarchy document’s clustering based on fuzzy luhn’s ...IJECEIAES

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A template based algorithm for automatic summarization and dialogue managemen...eSAT Journals

Abstract This paper describes an automated approach for extracting significant and useful events from unstructured text. The goal of research is to come out with a methodology which helps in extracting important events such as dates, places, and subjects of interest. It would be also convenient if the methodology helps in presenting the users with a shorter version of the text which contain all non-trivial information. We also discuss implementation of algorithms which exactly does this task, developed by us. Key Words: Cosine Similarity, Information, Natural Language, Summarization, Text Mining

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Design of optimal search engine using text summarization through artificial i...TELKOMNIKA JOURNAL

Natural language processing is the trending topic in the latest research areas, which allows the developers to create the human-computer interactions to come into existence. The natural language processing is an integration of artificial intelligence, computer science and computer linguistics. The research towards natural Language Processing is focused on creating innovations towards creating the devices or machines which operates basing on the single command of a human. It allows various Bot creations to innovate the instructions from the mobile devices to control the physical devices by allowing the speech-tagging. In our paper, we design a search engine which not only displays the data according to user query but also performs the detailed display of the content or topic user is interested for using the summarization concept. We find the designed search engine is having optimal response time for the user queries by analyzing with number of transactions as inputs. Also, the result findings in the performance analysis show that the text summarization method has been an efficient way for improving the response time in the search engine optimizations.

Y24168171IJERA Editor

This document discusses using the Expectation Maximization clustering algorithm for text summarization. It begins with an introduction to text summarization and natural language processing. It then describes implementing Expectation Maximization clustering on text that has undergone natural language processing steps like splitting, tokenization, part-of-speech tagging, and parsing. This clusters similar sentences based on their similarity values from the distance matrix. The clustered sentences can then be used to generate a summary by selecting the most representative sentences.

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[IJET-V2I3P19] Authors: Priyanka SharmaIJET - International Journal of Engineering and Techniques

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Information Retrieval System is an effective process that helps a user to trace relevant information by Natural Language Processing (NLP). In this research paper, we have presented present an algorithmic Information Retrieval System(BIRS) based on information and the system is significant mathematically and statistically. This paper is demonstrated by two algorithms for finding out the lemmatization of Bengali words such as Trie and Dictionary Based Search by Removing Affix (DBSRA) as well as compared with Edit Distance for the exact lemmatization. We have presented the Bengali Anaphora resolution system using the Hobbs’ algorithm to get the correct expression of information. As the actions of questions answering algorithms, the TF-IDF and Cosine Similarity are developed to find out the accurate answer from the documents. In this study, we have introduced a Bengali Language Toolkit (BLTK) and Bengali Language Expression (BRE) that make the easiest implication of our task. We have also developed Bengali root word’s corpus, synonym word’s corpus, stop word’s corpus and gathered 672 articles from the popular Bengali newspapers ‘The Daily Prothom Alo’ which is our inserted information. For testing this system, we have created 19335 questions from the introduced information and got 97.22% accurate answer.

Dr31564567IJMER

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Correlation Preserving Indexing Based Text ClusteringIOSR Journals

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A rough set based hybrid method to text categorizationNinad Samel

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Sentence Extraction Based on Sentence Distribution and Part of Speech Tagging...TELKOMNIKA JOURNAL

Automatic multi-document summarization needs to find representative sentences not only by sentence distribution to select the most important sentence but also by how informative a term is in a sentence. Sentence distribution is suitable for obtaining important sentences by determining frequent and well-spread words in the corpus but ignores the grammatical information that indicates instructive content. The presence or absence of informative content in a sentence can be indicated by grammatical information which is carried by part of speech (POS) labels. In this paper, we propose a new sentence weighting method by incorporating sentence distribution and POS tagging for multi -document summarization. Similarity-based Histogram Clustering (SHC) is used to cluster sentences in the data set. Cluster ordering is based on cluster importance to determine the important clusters. Sentence extraction based on sentence distribution and POS tagging is introduced to extract the representative sentences from the ordered clusters. The results of the experiment on the Document Understanding Conferences (DUC) 2004 are compared with those of the Sentence Distribution Method. Our proposed method achieved better results with an increasing rate of 5.41% on ROUGE-1 and 0.62% on ROUGE-2.

[IJET-V1I6P17] Authors : Mrs.R.Kalpana, Mrs.P.PadmapriyaIJET - International Journal of Engineering and Techniques

Text mining efforts to innovate new, previous unknown or hidden data by automatically extracting collection of information from various written resources. Applying knowledge detection method to formless text is known as Knowledge Discovery in Text or Text data mining and also called Text Mining. Most of the techniques used in Text Mining are found on the statistical study of a term either word or phrase. There are different algorithms in Text mining are used in the previous method. For example Single-Link Algorithm and Self-Organizing Mapping(SOM) is introduces an approach for visualizing high-dimensional data and a very useful tool for processing textual data based on Projection method. Genetic and Sequential algorithms are provide the capability for multiscale representation of datasets and fast to compute with less CPU time based on the Isolet Reduces subsets in Unsupervised Feature Selection. We are going to propose the Vector Space Model and Concept based analysis algorithm it will improve the text clustering quality and a better text clustering result may achieve. We think it is a good behavior of the proposed algorithm is in terms of toughness and constancy with respect to the formation of Neural Network.

Mining Users Rare Sequential Topic Patterns from Tweets based on Topic Extrac...IRJET Journal

This paper proposes a method to mine rare sequential topic patterns (URSTPs) from tweet data. It involves preprocessing tweets to extract topics, identifying user sessions, generating sequential topic pattern (STP) candidates, and selecting URSTPs based on rarity analysis. Experiments show the approach can identify special users and interpretable URSTPs, indicating users' characteristics. The paper aims to capture personalized and abnormal user behaviors through sequential relationships between extracted topics from successive tweets.

Summarization in Computational linguisticsAhmad Mashhood

The document discusses summarization of single documents, specifically technical articles. It defines summarization as presenting a significant portion of a text's information in a shorter, abridged form. The advancement of computer processing systems and natural language processing enabled automated summarization through tools that can produce abstractive summaries. Automated summarization aims to generate summaries similar to human summaries to help address the large amount of online information. Single document summarization of technical articles typically involves extracting sentences while reorganizing and modifying them to form a coherent summary.

AbstractiveSurvey of text in today timefNidaShafique8

Conceptual framework for abstractive text summarizationijnlc

As the volume of information available on the Internet increases, there is a growing need for tools helping users to find, filter and manage these resources. While more and more textual information is available on-line, effective retrieval is difficult without proper indexing and summarization of the content. One of the possible solutions to this problem is abstractive text summarization. The idea is to propose a system that will accept single document as input in English and processes the input by building a rich semantic graph and then reducing this graph for generating the final summary.

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