The ability to identify important entities in a text, known as Named Entity Recognition (NER), is useful in a large variety of downstream tasks in the biomedical domain. This is a considerably difficult task when working with Consumer Health Questions (CHQs), which consist of informal language used in day-to-day life by patients. These difficulties are amplified in the case of Bengali, which allows for a huge amount of flexibility in sentence structures and has significant variances in regional dialects. Unfortunately, the complexity of the language is not accurately reflected in the limited amount of available data, which makes it difficult to build a reliable decision-making system. To address the scarcity of data, this paper presents ‘Bangla-HealthNER’, a comprehensive dataset designed to identify named entities in health-related texts in the Bengali language. It consists of 31,783 samples sourced from a popular online public health platform, which allows it to capture the diverse range of linguistic styles and dialects used by native speakers from various regions in their day-to-day lives. The insight into this diversity in language will prove useful to any medical decision-making systems that are developed for use in real-world applications. To highlight the difficulty of the dataset, it has been benchmarked on state-of-the-art token classification models, where BanglishBERT achieved the highest performance with an F1-score of 56.13 ± 0.75%. The dataset and all relevant code used in this work have been made publicly available.

Online health consultation is steadily gaining popularity as a platform for patients to discuss their medical health inquiries, known as Consumer Health Questions (CHQs). The emergence of the COVID-19 pandemic has also led to a surge in the use of such platforms, creating a significant burden for the limited number of healthcare professionals attempting to respond to the influx of questions. Abstractive text summarization is a promising solution to this challenge, since shortening CHQs to only the information essential to answering them reduces the amount of time spent parsing unnecessary information. The summarization process can also serve as an intermediate step towards the eventual development of an automated medical question-answering system. This paper presents ‘BanglaCHQ-Summ’, the first CHQ summarization dataset for the Bangla language, consisting of 2,350 question-summary pairs. It is benchmarked on state-of-the-art Bangla and multilingual text generation models, with the best-performing model, BanglaT5, achieving a ROUGE-L score of 48.35%. In addition, we address the limitations of existing automatic metrics for summarization by conducting a human evaluation. The dataset and all relevant code used in this work have been made publicly available.