Addressing the Problem of Coherence in Automatic Text Summarization: A Latent Semantic Analysis Approach

Abstract

This article is concerned with addressing the problem of coherence in the automatic summarization of prose fiction texts. Despite the increasing advances within the summarization theory, applications and industry, many problems are still unresolved in relations to the applications of the summarization theory to literature. This can be in part attributed to the peculiar nature of literary texts where standard or typical summarization processes are not amenable for literature. This study, therefore, tends to bridge the gap between literature and summarization theory by proposing a summarization system that is based on more semantic-based approaches for extracting more meaningful and coherent summaries. Given that lack of coherence within summaries has its negative implications on understanding original texts; it follows that more effective methods should be developed in relation to the extraction of coherent summaries. In order to do this, a hybrid of methods including statistical (TF-IDF) and semantic (Latent Semantic Analysis LSA) methods were used to derive the most distinctive features and extract summaries from 10 English novellas. For evaluation purposes, both intrinsic and extrinsic methods are used for determining the quality of the extracted summaries. Results indicate that the integration of LSA into features extraction methods achieves better summarization performance outcomes in terms of coherence properties within the extracted summaries.

An Evaluation of Automatic Text Summarization of News Articles: The Case of Three Online Arabic Text Summary Generators

“…Sentences are not well connected to one another, which has adverse impacts on the readability and understanding what the original text about, as shown in Box 1. Omar [42] explains that if sentences and clauses in the automatic summaries are not connected, the overall argumentative structure of the text is not supported and the 95 | P a g e www.ijacsa.thesai.org thematic significance of the original texts is lost. He adds that in many cases automatic summaries generated in this fashion are misleading for readers and users.…”