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We develop an abstractive summarization framework independent of labeled datafor multiple heterogeneous documents. Unlike existing multi-documentsummarization methods, our framework processes documents telling differentstories instead of documents on the same topic. We also enhance an existingsentence fusion method with a uni-directional language model to prioritizefused sentences with higher sentence probability with the goal of increasingreadability. Lastly, we construct a total of twelve dataset variations based onCNN/Daily Mail and the NewsRoom datasets, where each document group contains alarge and diverse collection of documents to evaluate the performance of ourmodel in comparison with other baseline systems. Our experiments demonstratethat our framework outperforms current state-of-the-art methods in this moregeneric setting.

Large-Scale Multi-Document Summarization with Information Extraction and Compression