Redundancy Removal Method for Multi-Document Query-Based Text Summarization

2021 ◽  
Author(s):  
Nazreena Rahman ◽  
Bhogeswar Borah
Keyword(s):  
Text Summarization ◽  
Removal Method ◽  
Redundancy Removal

A Temporal Redundancy Removal Method for Video Sequences using Feature Information

2013 ◽  
Author(s):  
Jae-Hyun Jun ◽  
◽  
Min-Jun Kim ◽  
Yong-Suk Jan ◽  
Cheol-Woong Ahn ◽  
...  
Keyword(s):  
Video Sequences ◽  
Removal Method ◽  
Redundancy Removal ◽  
Feature Information ◽  
Temporal Redundancy

scholarly journals Exploiting Semantic Term Relations in Text Summarization

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Similarity Measure ◽  
Text Summarization ◽  
Experimental Results ◽  
Semantic Relations ◽  
Document Summarization ◽  
Term Frequency ◽  
Redundancy Removal ◽  
Term Similarity ◽  
Summarization System

The traditional frequency based approach to creating multi-document extractive summary ranks sentences based on scores computed by summing up TF*IDF weights of words contained in the sentences. In this approach, TF or term frequency is calculated based on how frequently a term (word) occurs in the input and TF calculated in this way does not take into account the semantic relations among terms. In this paper, we propose methods that exploits semantic term relations for improving sentence ranking and redundancy removal steps of a summarization system. Our proposed summarization system has been tested on DUC 2003 and DUC 2004 benchmark multi-document summarization datasets. The experimental results reveal that performance of our multi-document text summarizer is significantly improved when the distributional term similarity measure is used for finding semantic term relations. Our multi-document text summarizer also outperforms some well known summarization baselines to which it is compared.

scholarly journals Query-Based Extractive Text Summarization Using Sense-Oriented Semantic Relatedness Measure

2021 ◽  
Author(s):  
Nazreena Rahman ◽  
Bhogeswar Borah
Keyword(s):  
Learning Algorithm ◽  
Word Sense Disambiguation ◽  
Semantic Relatedness ◽  
Text Summarization ◽  
Word Sense ◽  
Redundancy Removal ◽  
Summarization Method ◽  
Sense Disambiguation ◽  
Removal Technique ◽  
Current Systems

Abstract This paper presents a query-based extractive text summarization method by using sense-oriented semantic relatedness measure. We have proposed a Word Sense Disambiguation (WSD) technique to find the exact sense of a word present in the sentence. It helps in extracting query relevance sentences while calculating the sense-oriented sentence semantic relatedness score between the query and input text sentence. The proposed method uses five unique features to make clusters of query-relevant sentences. A redundancy removal technique is also put forward to eliminate redundant sentences. We have evaluated our proposed WSD technique with other existing methods by using Senseval and SemEval datasets. Experimental evaluation and discussion signifies the better performance of proposed WSD method over current systems in terms of F-score. We compare our proposed query-based extractive text summarization method with other methods participated in Document Understanding Conference (DUC) and as well as with current methods. Evaluation and comparison state that the proposed query-based extractive text summarization method outperforms many existing methods. As an unsupervised learning algorithm, we obtained highest ROUGE (Recall-Oriented Understudy for Gisting Evaluation) score for all three DUC 2005, 2006 and 2007 datasets. Our proposed method is also quite comparable with other supervised learning based algorithms. We also observe that our query-based extractive text summarization method can recognize query relevance sentences which meet the query need.

Extractive Technique for Text Summarization based on Ranking Scheme

2018 ◽  
Vol 6 (4) ◽  
pp. 369-373
Author(s):  
A.A. Shrivastava ◽  
◽  
◽  
◽  
A.S. Bagora ◽  
...  
Keyword(s):  
Text Summarization ◽  
Ranking Scheme

An Alternative to High-Temperature and Acid/Solvent-Based Methods for Removing Integrated Circuits from Ceramic or Other Problem Substrates

2005 ◽  
Author(s):  
Carl M. Nail
Keyword(s):  
High Temperature ◽  
Integrated Circuits ◽  
Success Rate ◽  
Mechanical Damage ◽  
Test Equipment ◽  
High Success Rate ◽  
Automated Test Equipment ◽  
Removal Method ◽  
Automated Test ◽  
Conventional Methods

Abstract Dice must often be removed from their packages and reassembled into more suitable packages for them to be tested in automated test equipment (ATE). Removing bare dice from their substrates using conventional methods poses risks for chemical, thermal, and/or mechanical damage. A new removal method is offered using metallography-based and parallel polishing-based techniques to remove the substrate while exposing the die to minimized risk for damage. This method has been tested and found to have a high success rate once the techniques are learned.

A Residual Chlorine Removal Method to Allow Drinking Water Monitoring by Biological Early Warning Systems

2005 ◽  
Author(s):  
Willian H. VAN DER Schalie ◽  
David E. Trader ◽  
Mark W. Widder ◽  
Tommy R. Shedd ◽  
Linda M. Brennan
Keyword(s):  
Drinking Water ◽  
Early Warning ◽  
Early Warning Systems ◽  
Water Monitoring ◽  
Residual Chlorine ◽  
Warning Systems ◽  
Removal Method ◽  
Chlorine Removal

An Automatic Text Summarization Method with the Concern of Covering Complete Formation

2020 ◽  
Vol 13 (5) ◽  
pp. 977-986
Author(s):  
Srinivasa Rao Kongara ◽  
Dasika Sree Rama Chandra Murthy ◽  
Gangadhara Rao Kancherla
Keyword(s):  
Research Method ◽  
Research Work ◽  
Fuzzy Rule ◽  
Text Summarization ◽  
Document Summarization ◽  
Summarization Method ◽  
Overall Evaluation ◽  
Multiple Documents ◽  
Rule System ◽  
Value Decomposition

Background: Text summarization is the process of generating a short description of the entire document which is more difficult to read. This method provides a convenient way of extracting the most useful information and a short summary of the documents. In the existing research work, this is focused by introducing the Fuzzy Rule-based Automated Summarization Method (FRASM). Existing work tends to have various limitations which might limit its applicability to the various real-world applications. The existing method is only suitable for the single document summarization where various applications such as research industries tend to summarize information from multiple documents. Methods: This paper proposed Multi-document Automated Summarization Method (MDASM) to introduce the summarization framework which would result in the accurate summarized outcome from the multiple documents. In this work, multi-document summarization is performed whereas in the existing system only single document summarization was performed. Initially document clustering is performed using modified k means cluster algorithm to group the similar kind of documents that provides the same meaning. This is identified by measuring the frequent term measurement. After clustering, pre-processing is performed by introducing the Hybrid TF-IDF and Singular value decomposition technique which would eliminate the irrelevant content and would result in the required content. Then sentence measurement is one by introducing the additional metrics namely Title measurement in addition to the existing work metrics to accurately retrieve the sentences with more similarity. Finally, a fuzzy rule system is applied to perform text summarization. Results: The overall evaluation of the research work is conducted in the MatLab simulation environment from which it is proved that the proposed research method ensures the optimal outcome than the existing research method in terms of accurate summarization. MDASM produces 89.28% increased accuracy, 89.28% increased precision, 89.36% increased recall value and 70% increased the f-measure value which performs better than FRASM. Conclusion: The summarization processes carried out in this work provides the accurate summarized outcome.

Designing a Chat-Bot for College Information using Information Retrieval and Automatic Text Summarization Techniques

2020 ◽  
Vol 01 ◽  
Author(s):  
Radha Guha
Keyword(s):  
Information Retrieval ◽  
Language Processing ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Text Summarization ◽  
The Internet ◽  
Specific Domain ◽  
User Query ◽  
College Information ◽  
Chat Bot

Background:: In the era of information overload it is very difficult for a human reader to make sense of the vast information available in the internet quickly. Even for a specific domain like college or university website it may be difficult for a user to browse through all the links to get the relevant answers quickly. Objective:: In this scenario, design of a chat-bot which can answer questions related to college information and compare between colleges will be very useful and novel. Methods:: In this paper a novel conversational interface chat-bot application with information retrieval and text summariza-tion skill is designed and implemented. Firstly this chat-bot has a simple dialog skill when it can understand the user query intent, it responds from the stored collection of answers. Secondly for unknown queries, this chat-bot can search the internet and then perform text summarization using advanced techniques of natural language processing (NLP) and text mining (TM). Results:: The advancement of NLP capability of information retrieval and text summarization using machine learning tech-niques of Latent Semantic Analysis(LSI), Latent Dirichlet Allocation (LDA), Word2Vec, Global Vector (GloVe) and Tex-tRank are reviewed and compared in this paper first before implementing them for the chat-bot design. This chat-bot im-proves user experience tremendously by getting answers to specific queries concisely which takes less time than to read the entire document. Students, parents and faculty can get the answers for variety of information like admission criteria, fees, course offerings, notice board, attendance, grades, placements, faculty profile, research papers and patents etc. more effi-ciently. Conclusion:: The purpose of this paper was to follow the advancement in NLP technologies and implement them in a novel application.

A bootstrapping approach for robust topic analysis

2002 ◽  
Vol 8 (2-3) ◽  
pp. 209-233 ◽  
Author(s):  
OLIVIER FERRET ◽  
BRIGITTE GRAU
Keyword(s):  
Information Extraction ◽  
Large Scale ◽  
Text Summarization ◽  
Great Precision ◽  
Topic Analysis ◽  
Structured Knowledge

Topic analysis is important for many applications dealing with texts, such as text summarization or information extraction. However, it can be done with great precision only if it relies on structured knowledge, which is difficult to produce on a large scale. In this paper, we propose using bootstrapping to solve this problem: a first topic analysis based on a weakly structured source of knowledge, a collocation network, is used for learning explicit topic representations that then support a more precise and reliable topic analysis.

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