NLP: Text summarization by frequency and sentence position methods

Abstract

In today's fast-growing online information age we have an abundance of text, especially on the web. New information is constantly being generated. Often due to time constraints we are not able to consume all the data available. It is therefore essential to be able to summarize the text so that it becomes easier to ingest, while maintaining the essence and understandability of the information. The summarizer basically uses the combinations of term frequency and sentence position methods with language specific lexicons in order to identify the most important sentence for extractive summary. We aim to design an algorithm that can summarize a document by their performance both objectively and subjectively in Afan Oromo Language. The performance of the summarizers was measured based on subjective as well as objective evaluation methods. The techniques used in this paper are term frequency and sentence position methods with language specific lexicons to assign weights to the sentences to be extracted for the summary.