385 citations · 645 across the 22 of their papers we have counts for
6 papers · 1 filter
SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization
Philippe Laban, Tobias Schnabel, Paul N. Bennett +1
In the summarization domain, a key requirement for summaries is to be factually consistent with the input document. Previous work has found that natural language inference (NLI) mo…
Keep it Simple: Unsupervised Simplification of Multi-Paragraph Text
Philippe Laban, Tobias Schnabel, Paul Bennett +1
This work presents Keep it Simple (KiS), a new approach to unsupervised text simplification which learns to balance a reward across three properties: fluency, salience and simplici…
What's The Latest? A Question-driven News Chatbot
Philippe Laban, John Canny, Marti A. Hearst
This work describes an automatic news chatbot that draws content from a diverse set of news articles and creates conversations with a user about the news. Key components of the sys…
News Headline Grouping as a Challenging NLU Task
Philippe Laban, Lucas Bandarkar, Marti A. Hearst
Recent progress in Natural Language Understanding (NLU) has seen the latest models outperform human performance on many standard tasks. These impressive results have led the commun…
The Summary Loop: Learning to Write Abstractive Summaries Without Examples
Philippe Laban, Andrew Hsi, John Canny +1
This work presents a new approach to unsupervised abstractive summarization based on maximizing a combination of coverage and fluency for a given length constraint. It introduces a…
Lux: Always-on Visualization Recommendations for Exploratory Dataframe Workflows
Doris Jung-Lin Lee, Dixin Tang, Kunal Agarwal +8
Exploratory data science largely happens in computational notebooks with dataframe APIs, such as pandas, that support flexible means to transform, clean, and analyze data. Yet, vis…