An Approach to Automatic Text Summarization Using Simplified Lesk Algorithm and Wordnet

Abstract

Text Summarization is a way to produce a text, which contains the significant portion of information of the original text(s). Different methodologies are developed till now depending upon several parameters to find the summary as the position, format and type of the sentences in an input text, formats of different words, frequency of a particular word in a text etc. But according to different languages and input sources, these parameters are varied. As result the performance of the algorithm is greatly affected. The proposed approach summarizes a text without depending upon those parameters. Here, the relevance of the sentences within the text is derived by Simplified Lesk algorithm and WordNet, an online dictionary. This approach is not only independent of the format of the text and position of a sentence in a text, as the sentences are arranged at first according to their relevance before the summarization process, the percentage of summarization can be varied according to needs. The proposed approach gives around 80% accurate results on 50% summarization of the original text with respect to the manually summarized result, performed on 50 different types and lengths of texts. We have achieved satisfactory results even upto 25% summarization of the original text.

Survey of Scientific Document Summarization Techniques

“…The Simplified Lesk algorithm and WordNet are used to extract relevant sentences from a document [57]. The definitions of all meaningful words from a sentence of the document are taken from WordNet, and an intersection operation is performed between each of these meanings and the original sentence.…”

Applications of Mining Arabic Text: A Review

“…It is important for different applications in natural language processing (NLP) such as information retrieval, question answering, and text classification systems. These applications can save time and resources, having their actual input text in condensed forms [50].…”

Towards Personalized Summaries in Spanish based on Learning Styles Theory

“…Current research proposes several and diverse methods for automatic text summarization such as statistical [22], machine learning [23,24], text connectivity [25,26], conceptual graphs [27,28,29], algebraic reduction [30], clustering and probabilistic models [31,32,33] and methods adapted to the reader [34,35].…”