Petros Koumoutsakos, Harvard School of Engineering and Applied Sciences,
https://seas.harvard.edu/person/petros-koumoutsakos
Title: Artificial Intelligence and Computational Science: There is Plenty of Room in the Middle
Abstract
Unprecedented hardware capabilities and algorithmic innovations have enabled the acquisition and analysis of massive datasets and simulations of complex systems that were inconceivable only a decade ago.
Computing is transforming our intellectual capacity to tackle complex problems and fueling the Artificial Intelligence (AI) revolution that is changing our world.
Computational Science and AI have been drivers and benefactors of these advances, each in different ways, and originally, with different targets.
I will juxtapose pattern recognition with Learning of Effective Dynamics, physics based flow control and controllers learned via multi-agent reinforcement learning, to argue and that the intellectual space between these two fields contains a wealth of opportunities for advancing human knowledge and scientific discovery.
Grigorios Tsoumakas, Department of Informatics, Aristotle University of Thessaloniki,
https://intelligence.csd.auth.gr/people/tsoumakas/
Title: Neural Abstractive Summarization: Methods and Applications
Abstract
This talk reviews past and recent work of our team on the topic of neural abstractive summarization. We will first present our divide-and-conquer approach for dealing with long documents and its application to summarizing scientific articles [1]. We will then discuss Bayesian active summarization, our approach to combining active learning with state-of-the-art summarization models [2,3]. Next, we will share our methods towards controlling the output of summarization models given a particular context, such as a topic, along with our corresponding evaluation metric [4]. Finally, we will present applications in healthcare and finance [5,6].
References
[1] Gidiotis, A., & Tsoumakas, G. (2020). A Divide-and-Conquer Approach to the Summarization of Long Documents. IEEE/ACM Transactions on Audio Speech and Language Processing, 28, 3029–3040. https://doi.org/10.1109/TASLP.2020.3037401
[2] Gidiotis, A., & Tsoumakas, G. (2022). Should We Trust This Summary? Bayesian Abstractive Summarization to The Rescue. Proceedings of the Annual Meeting of the Association for Computational Linguistics, 4119–4131. https://doi.org/10.18653/V1/2022.FINDINGS-ACL.325
[3] Gidiotis, A., & Tsoumakas, G. (2024). Bayesian active summarization. Computer Speech & Language, 83, 101553. https://doi.org/10.1016/J.CSL.2023.101553
[4] Passali, T., & Tsoumakas, G. (2024) Topic-Controllable Summarization: Topic-Aware Evaluation and Transformer Methods. Proceedings of LREC COLING 2024 (to appear). https://arxiv.org/abs/2206.04317
[5] Giannouris, P., Myridis, T., Passali, T., & Tsoumakas G. (2024) Plain Language Summarization of Clinical Trials. Proceedings of LREC COLING 2024 DeTermIt! Workshop on Evaluating Text Difficulty in a Multilingual Context (to appear).
[6] Passali, T., Gidiotis, A., Chatzikyriakidis, E., & Tsoumakas, G. (2021). Towards Human-Centered Summarization: A Case Study on Financial News. Bridging Human-Computer Interaction and Natural Language Processing, HCINLP 2021 – Proceedings of the 1st Workshop.
Stefanos Zafeiriou, Faculty of Engineering, Department of Computing, Imperial College,
https://www.imperial.ac.uk/people/s.zafeiriou
Title: To be announced.