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UBC Theses and Dissertations

A visual interface for browsing and summarizing conversations Rashid, Shama


In our daily lives, we have conversations with others in many different modalities like meetings, emails, chats, blogs etc. At the advent of the Web, the volume and the complexity of the conversational data generated through our day to day communication have increased many folds. A way to deal with this overwhelming amount of interactional information is to use automatic summarization for quick access. Although Machine Learning approaches can be used to generate automatic summaries, extractive or abstractive, they still have not reached the level of quality of human generated summaries. We introduce here a visual interface that takes advantage of human cognition and perception abilities in conjunction with automatically extracted knowledge concepts for the conversation to analyze it and to automatically generate a summary for it. Our interface provides the user an overview of the conversation's content and a way to quickly explore it. It aids to identify informative sentences as potential components of the summary based on visual cues. Our objective is to provide the user more control over choosing the topics she wants to appear in the concise resultant overview generated through interactive exploration, thus generating a focused summary. We use an ontology containing nodes for speakers, dialogue acts (DA), and a list of entities referred to in the conversation to provide entry points to the conversation. These concepts in the ontology are derived using classifiers based on generic features making it possible to use the interface to explore any mode of conversational data. In this thesis, we have designed an interface based on the principles of Natural Language Processing, Human Computer Interaction, and Information Visualization that can be used to browse a human conversation using the mapping of sentences to those ontology concepts and can be used to generate a brief and focused summary for the conversation. We have evaluated our interface in a formal user study and have found that our interface facilitates widely varying approaches adopted by people trying to analyze a conversation.

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