Automated Summarization of E-Commerce Application Reviews for Generating Application Descriptions to Enhance Customer Insights
Jessica Berliani, Alicia Jocelyn Siahaya, Lili Ayu Wulandhari, Ghinaa Zain Nabiilah · 2025
In the era of digital commerce, online reviews play a vital role in shaping consumer decisions and perceptions, particularly on major e-commerce platforms like Tokopedia and Shopee in Indonesia. However, despite their popularity, the overwhelming volume of user-submitted reviews can make it difficult for consumers to quickly identify relevant insights. This research addresses this issue by focusing on abstractive text summarization of user reviews, specifically using T5-small and DistilBART CNN 12-6 models. The methodology includes data collection from the Google Play Store, followed by preprocessing, manual summarization, and model training. The models are evaluated based on ROUGE metrics (ROUGE-1, ROUGE-2, and ROUGE-L) to assess their performance in creating coherent, human-like summaries. Results show that DistilBART CNN 12-6 outperforms T5-small in all evaluation metrics, with fine-tuning and hyperparameter adjustments enhancing both models. Stopword removal slightly improved DistilBART’s performance while having minimal impact on T5. The findings provide a foundation for developing summarization techniques that enhance the accessibility of user feedback, helping consumers make informed decisions while supporting application improvements