genre identification and goal-focused summarization
In this paper, we present a novel technique of first performing document genre identification, then utilizing the genre for producing tailored summaries based on a user's information seeking needs - genre oriented goal-focused summarization - such as a plot or opinion summary of a movie review. We create a test corpus to determine genre classification accuracy for 16 genres, and examine performance on various amounts of training data for machine learning algorithms - Random Forests, SVM light and Nave Bayes. Results show that Random Forests outperforms SVM light and Nave Bayes. The genre tag is used to inform a downstream summarization engine. We define types of summaries for 7 genres, create a ground truth corpus and analyze the results of genre oriented goal-focused summarization, showing that this type of user based summarization requires different algorithms than the leading sentence baseline which is known to perform well in the case of news articles.
genre identification summarization machine learning text categorization evaluation text classification metadata extraction task-based information retrieval data mining