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arXiv · 2205.00548

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

Ning Wang·Han Liu·Diego Klabjan
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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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Ning Wang, Han Liu, Diego Klabjan. 2022-05-01. Large-Scale Multi-Document Summarization with Information Extraction and Compression. https://arxiv.org/abs/2205.00548

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