PERFORMANCE ANALYSIS OF FEATURE EXTRACTION FOR ACCURATE AND PRECISE MULTI-DOCUMENT SUMMARIZATION

Abstract

Abstract: In recent years, abstractive text summarization using multimodal inputs has garnered significant attention from researchers due to its ability to synthesize information from various sources into concise summaries. Text summarization creates a concise version of the original document by identifying key information, but it is considered a general approach as it doesn’t capture the distribution of opinions or sentiments. In contrast, review summarization offers a detailed breakdown of product aspects and associated sentiments, helping online shoppers make informed decisions. Due to the informal style, short length, and unstructured nature of reviews, review summarization is challenging. This study introduces an aspect-based abstractive summarization method for customer reviews, utilizing an encoder-decoder model with attention and pointer generator networks. A Bi-GRU encoder-decoder ensures that adjacent words contribute to the summary's coherence. The proposed automatic text summarization is compared over the existing models in terms of performance measures like ROUGE metrics achieves high scores as R1 score 43.61, R2 score 22.64, R3 score 44.95 and RL score is 44.27 on Benchmark DUC datasets.