Abstractive and Extractive Approaches for Summarizing Multi-document Travel Reviews
Dublin Core
Title
Abstractive and Extractive Approaches for Summarizing Multi-document Travel Reviews
Subject
abstractive; extractive; summarization; bert; gpt2; clustering; sentiment; keyword
Description
Travel reviews offer insights into users' experiences at places they have visited, including hotels, restaurants, and tourist
attractions. Reviews are a type of multi-document, 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 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 BERTEXT, 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 are higher
performance than partial approach
attractions. Reviews are a type of multi-document, 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 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 BERTEXT, 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 are higher
performance than partial approach
Creator
Narandha Arya Ranggianto, Diana Purwitasari, Chastine Fatichah, Rizka Wakhidatus Sholikah
Source
http://jurnal.iaii.or.id
Publisher
Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association
Date
December 2023
Contributor
Sri Wahyuni
Rights
ISSN Media Electronic: 2580-0760
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Narandha Arya Ranggianto, Diana Purwitasari, Chastine Fatichah, Rizka Wakhidatus Sholikah, “Abstractive and Extractive Approaches for Summarizing Multi-document Travel Reviews,” Repository Horizon University Indonesia, accessed January 11, 2026, https://repository.horizon.ac.id/items/show/10125.