  This paper presents an unsupervised extractive approach to summarize
scientific long documents based on the Information Bottleneck principle.
Inspired by previous work which uses the Information Bottleneck principle for
sentence compression, we extend it to document level summarization with two
separate steps. In the first step, we use signal(s) as queries to retrieve the
key content from the source document. Then, a pre-trained language model
conducts further sentence search and edit to return the final extracted
summaries. Importantly, our work can be flexibly extended to a multi-view
framework by different signals. Automatic evaluation on three scientific
document datasets verifies the effectiveness of the proposed framework. The
further human evaluation suggests that the extracted summaries cover more
content aspects than previous systems.
