Evaluating Multilingual Abstractive Dialogue Summarization in Indian Languages using mT5-small & IndicBART

Every day, the internet is inundated with massive amounts of data. Unlike texts, such as academic reports and news that ordinarily originate from a single source and have a well-organized structure, dialogues involve the dynamic exchange of information between two or more speakers. The objective of a discussion may shift during the track of the conversation, and important information is dispersed across multiple speakers’, making abstractive summarization of dialogues demanding. Numerous summarizing approaches have been suggested and implemented for English and other foreign languages. On the other hand, the process of summarizing dialogue in Indian languages is still in its newborn stages. By providing insights into the performance of these models and how they adapt to a wide variety of linguistic nuances, the aim of this study is to shed light on the efficacy of these models. This work addresses the challenge of abstractive summarization of dialogues in three prominent Indian languages: Hindi, Marathi and Bengali by evaluating the effectiveness of two specific multilingual models, namely mT5-small and indicBART. According to the findings of the comparative analysis, the mT5-small model has greater accuracy in the three chosen languages.