Out of hundreds of applicants, Professor Marti Hearst is one of six recipients of a Bloomberg Data Science Research Grant for research on Unsupervised Abstractive News Summarization.
From Tech At Bloomberg
Since 2015, Bloomberg has supported academic research in broadly-construed data science, including natural language processing (NLP), information retrieval, machine learning, and data mining, through its annual Data Science Research Grant Program (learn more about prior grant recipients and their research).
Today, we are pleased to announce the winners of our sixth round of grants.
Out of hundreds of applications from university faculty members, a committee of Bloomberg’s data scientists from across the organization chose to fund the following six research projects:
Marti A. Hearst (University of California, Berkeley)
Unsupervised Abstractive News Summarization
Summarization is a critical problem for a wide variety of applications, including news. To date, most automated summarization algorithms have been extractive, meaning they extract sentences from the original document to create a summary. However, humans usually write abstractive summaries: they create novel sentences. With the advent of deep learning, automated abstractive approaches are only recently coming to the fore. Professor Hearst and Ph.D. student Philippe Laban have recently contributed to progress in this research stream, developing a new Transformer-based approach to abstractive summarization that includes a conceptually simple keyword-coverage algorithm and a method for generating two different summary formats. Their research will look at yet another novel idea, which is to take abstractive summarization from supervised to unsupervised learning using a novel architecture that leverages the output of a supervised model to bootstrap a new unsupervised reinforcement learning based approach.
Marti Hearst is a Professor at the UC Berkeley School of Information, with a focus on Human-computer Interaction (HCI), Information Retrieval & Search, and Information Visualization.