A System for Interactive Summarization of Large Text Collections
Abstract. There exists an ever-growing set of data-centric systems that allow data scientists of varying skill levels to interactively manipulate, analyze and explore large structured data sets. However, there are currently not many systems that allow data scientists and novice users to interactively explore unstructured text document collections.
In this demo paper, we present a new system for interactive text summarization called Sherlock. The task of producing textual summaries is an important step to understand a collection of multiple topic-related documents and has many real-world applications in journalism, medicine, and many more. However, none of the existing summarization systems allow users to provide feedback at interactive speed. We therefore integrated a new approximate summarization model into Sherlock that can guarantee interactive speeds even for large text collections to keep the user engaged in the process.