Paper Info
Title
Concept generalization and fusion for abstractive sentence generation.
Abstract
We present our methodology for the generalization and fusion of conceptsWe define the notion of sentence generalizationThis work should be useful in intelligent systemsWe have proposed three approaches to find the one that will perform betterThe ML-based approach has benefited from the strength of two features Text summarization is either extractive or abstractive. Extractive summarization is to select the most salient pieces of information (words, phrases, and/or sentences) from a source document without adding any external information. Abstractive summarization allows an internal representation of the source document so as to produce a faithful summary of the source. In this case, external text can be inserted into the generated summary. Because of the complexity of the abstractive approach, the vast majority of work in text summarization has adopted an extractive approach.In this work, we focus on concepts fusion and generalization, i.e. where different concepts appearing in a sentence can be replaced by one concept which covers the meanings of all of them. This is one operation that can be used as part of an abstractive text summarization system. The main goal of this contribution is to enrich the research efforts on abstractive text summarization with a novel approach that allows the generalization of sentences using semantic resources. This work should be useful in intelligent systems more generally since it introduces a means to shorten sentences by producing more general (hence abstractions of the) sentences. It could be used, for instance, to display shorter texts in applications for mobile devices. It should also improve the quality of the generated text summaries by mentioning key (general) concepts. One can think of using the approach in reasoning systems where different concepts appearing in the same context are related to one another with the aim of finding a more general representation of the concepts. This could be in the context of Goal Formulation, expert systems, scenario recognition, and cognitive reasoning more generally.We present our methodology for the generalization and fusion of concepts that appear in sentences. This is achieved through (1) the detection and extraction of what we define as generalizable sentences and (2) the generation and reduction of the space of generalization versions. We introduce two approaches we have designed to select the best sentences from the space of generalization versions. Using four NLTK11The Natural Language Tool Kit (http://nltk.org/) corpora, the first approach estimates the \"acceptability\" of a given generalization version. The second approach is Machine Learning-based and uses contextual and specific features. The recall, precision and F1-score measures resulting from the evaluation of the concept generalization and fusion approach are presented.
Year
DOI
Venue
2016
10.1016/j.eswa.2016.01.007
Expert Syst. Appl.
Keywords
Field
DocType
Artificial intelligence,Natural language processing,Text summarization,Concept fusion,Concept generalization,WordNet
Automatic summarization,Abstraction,Intelligent decision support system,Computer science,Expert system,Mobile device,Natural language,Natural language processing,Artificial intelligence,WordNet,Sentence,Machine learning
Journal
Volume
Issue
ISSN
53
C
0957-4174
Citations 
PageRank 
References 
2
0.35
28
Authors
2
Name
Order
Citations
PageRank
Riadh Belkebir1151.54
Ahmed Guessoum2114.97