Extractive multi document summarization using harmony search algorithm

The exponential growth of information on the internet makes it troublesome for users to get valuable information. Text summarization is the process to overcome such a problem. An adequate summary must have wide coverage, high diversity, and high readability. In this article, a new method for multi-document summarization has been supposed based on a harmony search algorithm that optimizes the coverage, diversity, and readability. Concerning the benchmark dataset Text Analysis Conference (TAC-2011), the ROUGE package used to measure the effectiveness of the proposed model. The calculated results support the effectiveness of the proposed approach

A graph-based approach towards automatic text summarization

Due to an exponential increase in number of electronic documents and easy access to information on the Internet, the need for text summarization has become obvious. An ideal summary contains important parts of the original document, eliminates redundant information and can be generated from single or multiple documents. There are several online text summarizers but they have limited accessibility and generate somewhat incoherent summaries. We have proposed a Graph-based Automatic Summarizer (GAUTOSUMM), which consists of a pre-processing module, control features and a post-processing module. For evaluation, two datasets, Opinosis and DUC 2007 are used and generated summaries are evaluated using ROUGE metrics. The results show that GAUTOSUMM outperforms the online text summarizers in eight out of ten topics both in terms of the summary quality and time performance. A user interface has also been built to collect the original text and the desired number of sentences in the summary.text summarizationgraph-basedautomatic text summarizationGAUTOSUM