With the ubiquitous use of online meeting platforms and robust automatic speech recognition systems, meeting transcripts have emerged as a new and interesting domain for natural language tasks. Most recent works on meeting transcripts are restricted to summarization and extraction of action items. However, meeting discussions also have a useful question-answering (QA) component, crucial to understanding the discourse or meeting content, and can be used to build interactive interfaces on top of long transcripts. Hence, in this work, we leverage this inherent QA component of meeting discussions and introduce MEETINGQA, an extractive QA dataset comprising of questions asked by meeting participants and corresponding responses. As a result, questions can be open-ended and seek active discussions, while the answers can be multi-span and spread across multiple speakers. Our comprehensive empirical study of several robust baselines in- cluding long-context language models and re- cent instruction-tuned models reveals that mod- els perform poorly on this task (F1 = 57.3) and severely lag behind human performance (F1 = 84.6), thus presenting a useful, challenging new task for the community to improve upon