Extraction Based Multi Document Summarization using Single Document Summary Cluster

Multi document summarization has very great impact among research community, ever since the growth of online information and availability. Selecting most important sentences from such huge repository of data is quiet tricky and challenging task. While multi document poses some additional overhead in sentence selection, generating summaries for each individual documents and merging the sentences in a coherent order would greater strength. The proposed approach was competitively better as compared to state of MEAD summarizer at focused compression ratios. This paper focus on three different studies namely i. To find the performance of multi document summarizer from single document cluster (using MEAD) ii. Comparison of our approach with MEAD performance for the dataset considered iii. To extract sentences for multi document summarization at 30% compression rate to obtain 100% efficiency using 7-point summary sheet. Investigation carried out from an average of 22 documents shows that our system is promising.