Extractive Text Summarization for Wolaita

Abstract

Text summarization is the mechanism of summarizing a huge document comprising vast amount of information which is difficult to overcome and understand its message easily in any written documents for whatever languages without losing its entire message. A short and precise document which conveys intended information for the user in demand is expected in this information age. In addition to that, summarizing a document with vast amount of information is very difficult and time consuming specially for less resourced and technologically unfavored languages. Therefore in this study, the researcher proposed to address such problems for Wolaita by using graph based extractive text summarization approach. To attain the goal of this study the researcher prepared 92 documents for the study, explored extractive text summarization with graph-based approach to address the problems, performed text preprocessing tasks and finally developed text summarization model by using TextRank algorithms. The researcher used 92 documents, performed 92 various experiments, on documents and experimental results and findings were discussed in detail. To evaluate the model performance, three different expert summaries were collected for documents and computed system generated summaries with ROUGE evaluation metric. The researcher justified it with ROUGE evaluation metrics by comparing the system summaries with the expert summaries. The result obtained from the experiment shows promising result in summarization of Wolaita text. Finally, the experimental result of a 61.16% recall, 60.69% precision and 60.46% f-measures were obtained.