Extractive Text Summarization Using Deep Learning for Tigrigna Language

Abstract

With the ever-increasing amounts of textual material such as web pages, news articles, blogs, microblogs, and similar, the Internet became the massive body of unstructured information. In this paper to deal with the issues for the availability of more and more information with less time, the extractive text summarization using the deep learning model was used. In this paper, the proposed approach uses three basic stages of feature extraction, feature enhancement, and summary generation of the given news article to extract the core information, to produce well understandable summary and save reader's time. In the feature extraction, We explore various features to improve the extracted sentences to the summary by the score and rank of the extracted features matrix by calculating the top thematic words, paragraph segmentation, sentences length & position, proper nouns, and TF-ISF, and the sum of the feature vector given to RBM to enhance the extracted feature vector and finally generate the final summarization by taking top high scores and 50% 0f the sum second higher scores from the enhanced feature extracted scores. For experimenting purpose, we have used 10 news articles from the total gathered news articles gathered from BBC-Tigrigna, Fana-Tigrigna and VOA-Tigrigna news website. The evaluation of the extracted summary was evaluated using Recall-Oriented Understudy for Gisting Evaluation (ROUGE) to compare the system extracted summary with the reference / manual summary prepared by human experts. According the experimentation, the average score of ROUG-1 shows 49% for recall, 39% precession, 42% for F-score and for the ROUGE-2 shows that 32% recall, 26% precession and 28% for F-score, for ROUGE-l also shows that 39% of recall, 33% of Precession, and 35% of F-scores. The result shows the proposed approach have higher result in Rouge-1 and the F-score or harmonic mean of precision and recall is 42% and it solves the problems of information overloading in the ever-increasing available news articles by generating the extractive summarizations.