Abstractive Text Summarization Using Enhanced Attention Model

2020 ◽  
pp. 63-76
Author(s):  
Rajendra Kumar Roul ◽  
Pratik Madhav Joshi ◽  
Jajati Keshari Sahoo
Keyword(s):  
Text Summarization ◽  
Attention Model

Abstractive social media text summarization using selective reinforced Seq2Seq attention model

2020 ◽  
Vol 410 ◽  
pp. 432-440 ◽  
Author(s):  
Zeyu Liang ◽  
Junping Du ◽  
Chaoyang Li
Keyword(s):  
Social Media ◽  
Text Summarization ◽  
Attention Model ◽  
Social Media Text

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

Fast Visual Attention Model Algorithm Based on Approximate Gaussian Pyramids

2009 ◽  
Vol 20 (12) ◽  
pp. 3240-3253 ◽  
Author(s):  
Guo-Min ZHANG ◽  
Jian-Ping YIN ◽  
En ZHU ◽  
Ling MAO
Keyword(s):  
Visual Attention ◽  
Visual Attention Model ◽  
Attention Model ◽  
Model Algorithm

Feedback Attention Model for Image Captioning

2019 ◽  
Vol 31 (7) ◽  
pp. 1122
Author(s):  
Fan Lyu ◽  
Fuyuan Hu ◽  
Yanning Zhang ◽  
Zhenping Xia ◽  
S Sheng Victor
Keyword(s):  
Image Captioning ◽  
Attention Model

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.

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