QFAS-KE: Query focused answer summarization using keyword extraction

Abstract

Question answering (QA) portals like Quora, Stack Overflow, AskUbuntu, Yahoo! Answers, Reddit, and Wiki Answers have emerged as hubs of curiosity, highlighting the rising demands for easily accessible information and are drawing focus to hundreds of millions of questions. The efficient utilization of these questions and associated answers has become significantly vital for these QA websites. The similarity-based information retrieval methods provide a ranked list of potentially relevant questions, and the users have to spend significant time sifting through the results to discover the best answer. This paper aims to provide a precise, comprehensive, summarized answer to the user asked query using extracted keywords that offer valuable insights into relevant content. The research work presents a Query focused Answer Summarization framework using Keyword Extraction (QFAS-KE). It is a four-stage framework, including query question pre-processing, semantic question search (utilizing SBERT and FAISS vector database), answer retrieval and re-ranking (utilizing BERT-based bi-encoder and cross-encoder), and answer summary generation (using fine-tuned transformers such as BART, PEGASUS, T5) with keyword guidance (using a keyword extractor such as KeyBERT). The results conceptualize the efficacy of the proposed framework on task-specific datasets (CNN/DailyMail and MS-MARCO) over the ROUGE metric. The model outperformed existing baseline models on CNN/DailyMail dataset with a value of 47.5 (PEGASUS), 46.2 (BART), and 45.1 (T5) in terms of ROUGE-1 and on MS-MARCO dataset with a value of 75.18 (PEGASUS), 79.02 (BART), and 74.69 (T5) in terms of ROUGE-L.