AN ABSTRACTIVE MULTI DOCUMENT SUMMARIZATION USING RECURRENT NEURAL NETWORK

Multi-document summarization system using Latent Dirichlet Allocation (LDA) model sounds promising. LDA is a powerful technique used for topic modelling, which can identify the underlying topics in a set of documents. By classifying documents into topics, the LDA model can help you select the most relevant sentences from each document for inclusion in the summary. In addition, you mentioned that you will be using Lex Rank to score the sentences and summaries of documents. Lex Rank is a graph-based ranking algorithm that has been proven to be effective in summarization tasks. By using Lex Rank, you can ensure that the most important and informative sentences are included in the summary. Our approach seems to be focused on generating informative and high-quality summaries while reducing the computational load.