University of Pittsburgh

Automatic Extractive Summarization of Trade Secret Misappropriation Cases

graduate student
Date: 
Friday, April 12, 2019 - 12:30pm - 1:00pm

Automatic extractive summarization is a process of selecting the most important passages from a document using a computer program. Given the rise of a large number of legal documents in electronic format, there is an increasing demand for effective information retrieval tools that present important information in a suitable user-friendly format. A team of CMU students and we carried out an experiment with a corpus of human-created summaries of trade secret misappropriation cases paired with their corresponding full text decisions. The team employed sentence classification, relevance/redundancy-based selection, and specialized word embeddings. The team applied both automatic evaluation metrics and expert grading to evaluate the automatically generated summaries along with human standard summaries. As the CMU students have now graduated, I will assist the instructor in rerunning the experiments and generating new results.  

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