MRST: A New Technique For Information Summarization

summarization is defined as " The process of concisely restating the essential ideas of a text or passage, and synthesizing the ideas into an overarching, idea ". There is an increasing need of coming up with ideas that can perfectly generate summaries as there is large repository of data present online but to get to the right information is indeed very difficult. In order to achieve this task many summarizing techniques have been devised such as LEAD, MEAD and RANDOM. The paper proposes a new technique for information summarization that basically combines the rhetorical structure theory with the MEAD summarizer system as Mead is based totally based on mathematical calculation and lack a knowledge base Rhetorical Structure Theory is used to overcome this weakness and in the end the new summarizer system is evaluated against the original MEAD summarizer system exploit mainly two areas of information that are Financial Articles and Medline abstracts. In theory MRST should be better than Mead but in practice Mead came ahead of MRST and that's merely because of one reason and that is there is no true parser that completely implements the Rhetorical Structure Theory. The results show that Mead produces successful summaries 75% time for both short and long documents. Incase of MRST it produces successful summaries for short documents 70% of the time and for long documents it produces successful summaries 65% of the time, as the size of the document increases the performance of MRST deteriorate. The main finding of the work is if we could come up with a parser that comprehensively implement the rhetorical structure theory then we would be able to come up the summarizer system that would be better then MEAD.