We present Web-STAR, an online platform for story understanding built on top of the STAR reasoning engine for STory comprehension through ARgumentation. The platform includes a web-based IDE, integration with the STAR system, and a web service infrastructure to support integration with other systems that rely on story understanding functionality to complete their tasks. The platform also delivers a number of "social" features, including a community repository for public story sharing with a built-in commenting system, and tools for collaborative story editing that can be used for team development projects and for educational purposes.
This paper describes initial work on Deep Read, an automated reading comprehension system that accepts arbitrary text input (a story) and answers questions about it. We have acquired a corpus of 60 development and 60 test stories of 3rd to 6th grade material; each story is followed by short-answer questions (an answer key was also provided). We used these to construct and evaluate a baseline system that uses pattern matching (bag-of-words) techniques augmented with additional automated linguistic processing (stemming, name identification, semantic class identification, and pronoun resolution). This simple system retrieves the sentence containing the answer 30--40 of the time. We have developed a rule-based system, Quarc, that can read a short story and find the sentence in the story that best answers a given question. Quarc uses heuristic rules that look for lexical and semantic clues in the question and the story. We have tested Quarc on reading comprehension tests typically given to children in grades 3--6. Overall, Quarc found the correct sentence 40 of the time, which is encouraging given the simplicity of its rules.