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

• URL: http://arxiv.org/abs/2205.00548v1
• Date: Sun, 1 May 2022 19:49:15 GMT
• Title: Large-Scale Multi-Document Summarization with Information Extraction and Compression
• Authors: Ning Wang, Han Liu, Diego Klabjan
• Abstract summary: We develop an abstractive summarization framework independent of labeled data for multiple heterogeneous documents. Our framework processes documents telling different stories instead of documents on the same topic. Our experiments demonstrate that our framework outperforms current state-of-the-art methods in this more generic setting.
• Score: 31.601707033466766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We develop an abstractive summarization framework independent of labeled data for multiple heterogeneous documents. Unlike existing multi-document summarization methods, our framework processes documents telling different stories instead of documents on the same topic. We also enhance an existing sentence fusion method with a uni-directional language model to prioritize fused sentences with higher sentence probability with the goal of increasing readability. Lastly, we construct a total of twelve dataset variations based on CNN/Daily Mail and the NewsRoom datasets, where each document group contains a large and diverse collection of documents to evaluate the performance of our model in comparison with other baseline systems. Our experiments demonstrate that our framework outperforms current state-of-the-art methods in this more generic setting.

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