Sentence compression reduces the length of text by removing non-essential content while preserving important facts and grammaticality.
Sequence-to-sequence (s2s) models are the basis for extensive work in natural language processing.
Multi-document summarization (MDS) aims to compress the content in large document collections into short summaries and has important applications in story clustering for newsfeeds, presentation of search results, and timeline generation.
Previous work on automatic news timeline summarization (TLS) leaves an unclear picture about how this task can generally be approached and how well it is currently solved.
The centroid-based model for extractive document summarization is a simple and fast baseline that ranks sentences based on their similarity to a centroid vector.