Abstractive and Extractive Approaches for Summarizing Multi-document Travel Reviews

Abstract

Travel reviews offer insights into users' experiences at places they have visited, including hotels, restaurants, and tourist attractions. Reviews are a type of multidocument, where one place has several reviews from different users. Automatic summarization can help users get the main information in multi-document. Automatic summarization consists of abstractive and extractive approaches. The abstractive approach has the advantage of producing coherent and concise sentences, while the extractive approach has the advantage of producing an informative summary. However, there are weaknesses in the abstractive approach, which results in inaccurate and less information. On the other hand, the extractive approach produces longer sentences compared to the abstractive approach. Based on the characteristics of both approaches, we combine abstractive and extractive methods to produce a more concise and informative summary than can be achieved using either approach alone. To assess the effectiveness of abstractive and extractive, we use ROUGE based on lexical overlaps and BERTScore based on contextual embeddings which it be compared with a partial approach (abstractive only or extractive only). The experimental results demonstrate that the combination of abstractive and extractive approaches, namely BERT-EXT, leads to improved performance. The ROUGE-1 (unigram), ROUGE-2 (bigram), ROUGE-L (longest subsequence), and BERTScore values are 29.48%, 5.76%, 33.59%, and 54.38%, respectively. Combining abstractive and extractive approach yields higher performance than the partial approach.

Keywords

• abstractive-extractive summarization
• bert
• gpt2
• clustering
• centroid
• sentiment-keyword