Automatic text summarization of news articles

Text Summarization has always been an area of active interest in the academia. In recent times, even though several techniques have being developed for automatic text summarization, efficiency is still a concern. Given the increase in size and number of documents available online, an efficient automatic news summarizer is the need of the hour. In this paper, we propose a technique of text summarization which focuses on the problem of identifying the most important portions of the text and producing coherent summaries. In our methodology, we donot require full semantic interpretation of the text, instead we create a summary using a model of topic progression in the text derived from lexical chains. We present an optimized and efficient algorithm to generate text summary using lexical chains and using the WordNet thesaurus. Further, we also overcome the limitations of the lexical chain approach to generate a good summary by implementing pronoun resolution and by suggesting new scoring techniques to leverage the structure of news articles.