scholarly journals Generating Indicative-Informative Summaries with SumUM

2002 ◽  
Vol 28 (4) ◽  
pp. 497-526 ◽  
Author(s):  
Horacio Saggion ◽  
Guy Lapalme
Keyword(s):  
Semantic Analysis ◽  
Text Summarization ◽  
Concept Identification ◽  
Human Judgment ◽  
Informative Part ◽  
Summarization System ◽  
Analysis Concept

We present and evaluate SumUM, a text summarization system that takes a raw technical text as input and produces an indicative informative summary. The indicative part of the summary identifies the topics of the document, and the informative part elaborates on some of these topics according to the reader's interest. SumUM motivates the topics, describes entities, and defines concepts. It is a first step for exploring the issue of dynamic summarization. This is accomplished through a process of shallow syntactic and semantic analysis, concept identification, and text regeneration. Our method was developed through the study of a corpus of abstracts written by professional abstractors. Relying on human judgment, we have evaluated indicativeness, informativeness, and text acceptability of the automatic summaries. The results thus far indicate good performance when compared with other summarization technologies.

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.

scholarly journals Automatic Text Summarization of COVID-19 Research Articles Using Recurrent Neural Networks and Coreference Resolution

2021 ◽  
Author(s):  
Mahsa Afsharizadeh ◽  
Hossein Ebrahimpour-Komleh ◽  
Ayoub Bagheri
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Text Summarization ◽  
Medical Community ◽  
Research Articles ◽  
Coreference Resolution ◽  
Extractive Summarization ◽  
Automatic Text Summarization ◽  
Summarization System ◽  
Automatic Text

Purpose: Pandemic COVID-19 has created an emergency for the medical community. Researchers require extensive study of scientific literature in order to discover drugs and vaccines. In this situation where every minute is valuable to save the lives of hundreds of people, a quick understanding of scientific articles will help the medical community. Automatic text summarization makes this possible. Materials and Methods: In this study, a recurrent neural network-based extractive summarization is proposed. The extractive method identifies the informative parts of the text. Recurrent neural network is very powerful for analyzing sequences such as text. The proposed method has three phases: sentence encoding, sentence ranking, and summary generation. To improve the performance of the summarization system, a coreference resolution procedure is used. Coreference resolution identifies the mentions in the text that refer to the same entity in the real world. This procedure helps to summarization process by discovering the central subject of the text. Results: The proposed method is evaluated on the COVID-19 research articles extracted from the CORD-19 dataset. The results show that the combination of using recurrent neural network and coreference resolution embedding vectors improves the performance of the summarization system. The Proposed method by achieving the value of ROUGE1-recall 0.53 demonstrates the improvement of summarization performance by using coreference resolution embedding vectors in the RNN-based summarization system. Conclusion: In this study, coreference information is stored in the form of coreference embedding vectors. Jointly use of recurrent neural network and coreference resolution results in an efficient summarization system.

Experiments on Applying a Text Summarization System for Question Answering

2007 ◽  
pp. 372-376
Author(s):  
Pedro Paulo Balage Filho ◽  
Vinícius Rodrigues de Uzêda ◽  
Thiago Alexandre Salgueiro Pardo ◽  
Maria das Graças Volpe Nunes
Keyword(s):  
Question Answering ◽  
Text Summarization ◽  
Summarization System

Semantic Analysis and Text Summarization in Socio-Technical Systems

2017 ◽  
pp. 243-281 ◽  
Author(s):  
Nina Rizun
Keyword(s):  
Decision Making ◽  
Text Mining ◽  
Semantic Analysis ◽  
Text Summarization ◽  
Technical System ◽  
Synergy Effect ◽  
Decision Making Processes ◽  
Multi Level ◽  
Technical Systems

In this chapter, the authors present the results of the development the text-mining methodology for increasing the reliability of the functioning of Socio-technical System (STS). Taking into account revealed strengths and weaknesses of Discriminant and Probabilistic approaches of Latent Semantic Relations analysis in of the abstracting and summarization projection, the Methodology of Two-level Single Document Summarization was developed. The Methodology assumes the following elements of novelty: based on obtaining a multi-level topical framework of the document (abstracting); uses the synergy effect of consistent usage the combination of two approaches for identification of conceptually significant elements of the text (summarization). The examples demonstrating the basic workability of proposed Methodology were presented. Such approaches should help human to increase the quality of supporting the decision-making processes of STS in real time.

scholarly journals Extractive text summarization system to aid data extraction from full text in systematic review development

2016 ◽  
Vol 64 ◽  
pp. 265-272 ◽  
Author(s):  
Duy Duc An Bui ◽  
Guilherme Del Fiol ◽  
John F. Hurdle ◽  
Siddhartha Jonnalagadda
Keyword(s):  
Systematic Review ◽  
Full Text ◽  
Data Extraction ◽  
Text Summarization ◽  
Summarization System
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