Interactive summarization for data filtering and triage

There is an increasing demand for content filtering and flagging on social media in relation to cybersecurity and social media conduct monitoring. This task is challenging and there is a large body of recent work that addresses it within the Natural Language and Video Processing communities. In this work, we propose two novel perspectives on this problem and provide preliminary evidence for their potential success. First, for text-based data, we utilize the current state of the art topic-based summarization algorithms and provide an interactive topic-conditioning approach to enable multiple summarizations based on different highlighted topics. Second, due to the interactivity aspect, we are able to characterize how this approach can be integrated within the process of a human analyst to improve both the quality of filtered data and the effort.