Robust natural language understanding involves the automatic extraction and representation of entities, events, and states from unstructured text. However, a significant portion of the knowledge required for human-level understanding is implicit in the text and can only be accessed through inference. In this work, we employ Probabilistic Soft Logic (PSL) as a framework for leveraging common-sense knowledge to support natural language understanding over procedural texts. Under this framework, we combine logical consistency constraints with succinct representations of commonsense knowledge to probabilistically model entity-centric stative information over time. We demonstrate the feasibility of using PSL to represent procedural stative knowledge through a scalability assessment over an in-house, multi-domain, synthetic dataset.

This paper introduces a new type of summarization task, known as microblog summarization, which aims to synthesize content from multiple microblog posts on the same topic into a human-readable prose description of fixed length. Our approach leverages (1) a generative model which induces event structures from text and (2) a user behavior model which captures how users convey relevant content.