A Hybrid Solution To Abstractive Multi-Document Summarization Using Supervised and Unsupervised Learning

• Published in: 2019 International Conference on Intelligent Computing and Control Systems (ICCS) pp. 566 - 570
• Main Authors: Bhagchandani, Gaurav, Bodra, Deep, Gangan, Abhishek, Mulla, Nikahat
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2019
• Subjects: Abstractive summarization; BLEU; clustering; Conferences; Control systems; Information technology; long short-term memory cell (LSTM); Measurement; multi-document; natural language processing; Neural networks; paraphrasing; Redundancy; redundancy detection; ROUGE; sentence compression; Training
• DOI: 10.1109/ICCS45141.2019.9065724

In this work, we aim to develop an abstractive summarization system in the multi-document setup. The main challenge in this kind of a system is the identification of redundant information. Our approach hybridizes three components, viz. Clustering, Word Graphs, Neural Networks. In clustering, all the information from multiple documents is divided amongst clusters based on context and importance analysis, such that each cluster possesses sentences of a similar context - Redundancy Identification. Further, Shortest Path Detection in Word Graphs reduces the text. Along with that, we use a sequence to sequence sentence compression and perform paraphrasing using Supervised Recurrent Neural Network to generate an almost completely abstractive summary. The dataset DUC 2004 that was used indicates that the proposed system outperforms other systems in terms of metrics like ROUGE [1] and BLEU [2] .