Speaker: Laura Dietz, Assistant Professor at the University of New Hampshire
Talk Title: Retrieve-and-generate: How to Automatically Create Relevant Articles
Date: Friday, February 26, 2021 - 1:00 - 2:00 PM EST (North American Eastern Standard Time) via Zoom
Zoom Access: Zoom Link and reach out to
Alex Taubman for the passcode.
Abstract: A lot of progress has been made towards answering specific formalized information needs, such as questions or detailed search queries. However users who familiarize themselves in a new domain would like to read overviews that explain "everything that one needs to know" about a topic, instead of having to ask questions one by one. So far, such users either find an overview article on the web or a wiki, or they are left to piece together this overview on their own. The vision of complex answer retrieval is to develop algorithms that can produce comprehensive overviews for given topics such as "Zika fever", "Green Sea Turtle", or "Reducing air pollution". The success of strong neural models for language generation suggest the feasibility of this idea. However, several tasks such as subtopic detection and story generation need to be addressed before retrieve-and-generate systems will provide information-rich, relevant, and useful overviews. This talk gives an overview of the research advances resulting from TREC Complex Answer Retrieval and the years since.
More information about the TREC CAR and the datasets are available at http://trec-car.cs.unh.edu.
Bio: Laura Dietz is an Assistant Professor at the University of New Hampshire, where she leads the lab for text retrieval, extraction, machine learning and analytics (TREMA). She organizes a tutorial/workshop series on Utilizing Knowledge Graphs in Text-centric Retrieval (KG4IR) and coordinates the TREC Complex Answer Retrieval Track. She received an NSF CAREER Award for utilizing fine-grained knowledge annotations in text understanding and retrieval. Previously, she was a research scientist at the Data and Web Science Group at Mannheim University and the Center for Intelligent Information Retrieval (CIIR) at UMass Amherst. She obtained her doctoral degree with a thesis on topic models for networked data from Max Planck Institute for Informatics.
More Info: https://www.cs.unh.edu/~dietz/.