N usa D ialogue: Dialogue Summarization and Generation for Underrepresented and Extremely Low-Resource Languages (2025)

Abstract: Developing dialogue summarization for extremely low-resource languages is a challenging task. We introduce NusaDialogue, a dialogue summarization dataset for three underrepresented languages in the Malayo-Polynesian language family: Minangkabau, Balinese, and Buginese. NusaDialogue covers 17 topics and 185 subtopics, with annotations provided by 73 native speakers. Additionally, we conducted experiments using fine-tuning on a specifically designed medium-sized language model for Indonesian, as well as zero- and few-shot learning on various multilingual large language models (LLMs). The results indicate that, for extremely low-resource languages such as Minangkabau, Balinese, and Buginese, the fine-tuning approach yields significantly higher performance compared to zero- and few-shot prompting, even when applied to LLMs with considerably larger parameter sizes.

## introduction large language models (llms) have brought remarkable progress in language processing technology attaining a high-quality language understanding and generation capability @xcite @xcite @xcite @xcite . nonetheless, the generalization toward low-resource languages is still lacking causing a huge disparity in the applicability and accessibility of llms in numerous underrepresented languages such as languages spoken in africa @xcite @xcite @xcite , and south-east asia @xcite @xcite @xcite @xcite . various efforts provide solutions to this problem by developing novel resources on these underrepresented languages @xcite @xcite @xcite . despite the incredible progress, most works focus on machine translation and simple language understanding tasks, such as sentiment analysis and topic classification. more complex tasks such as open-domain dialogue, task-oriented dialogue, and dialogue summarization, are still left behind for these underrepresented languages. the task coverage limitation leads to a poor evaluation suite for assessing the capability of llms in these underrepresented languages. moreover, most datasets on these underrepresented languages are developed through translating text from other higherresource languages resulting in a translationese corpus @xcite @xcite which is not ideal for representing these underrepresented languages. in this work, we develop nusadialogue, the first dialogue summarization dataset covering 3 underrepresented languages under the malayo-polynesian languages group, i.e., minangkabau (min), balinese (ban), and buginese (bug). nu-sadialogue is a human-annotated colloquial-styled dialogue summarization dataset covering 17 topics and 185 subtopics. the colloquial and nontranslationese annotation nature of nusadialogue makes it suitable for representing the actual day-today use of these underrepresented languages. we ensure that the dataset is annotated by a balanced number of male and female annotators to make the dataset represent a more balanced demography. we further analyze the annotator bias based on the choice of topics and the gender of speakers within a conversation and find out that, despite being regionally diverse, the gender bias in the languages contains huge similarities. this showcases that gender bias is not only affected by local cultural values but also by broader values such as shared geopolitical and historical values. additionally, when comparing with prior work on bias in high-resource languages such as english @xcite @xcite @xcite , despite having a smaller correlation, we still find numerous amount of similarities. this showcases the potential of extracting a different scope of bias, i.e., regional, national, or global, by analyzing the bias behavior of multilingual corpora. we summarize our contribution in four-fold: ## conclusion we introduce nusadialogue, the first high-quality dialogue summarization corpus covering three ex-tremely low-resource languages: minangkabau (min), balinese (ban), and buginese (bug). nusadialogue covers a diverse set of topics from general day-to-day conversation to specific topics such as science, history, and politics. using nusadialogue, we showcase that, despite having non-gendered pronouns, annotators still reflect gender bias in terms of role and topic selection which is propagated through person names and courtesy titles. furthermore, we develop the first dialogue-summarization benchmark for these languages, showcasing the inability of llms to generalize to these languages. lastly, we demonstrate a gender benchmark which showcases that llms do not have name bias o the languages under study due to the lack of representation of these languages.