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Found 5 papers, 4 papers with code
Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning
Sentence compression reduces the length of text by removing non-essential content while preserving important facts and grammaticality.
DynE: Dynamic Ensemble Decoding for Multi-Document Summarization
Sequence-to-sequence (s2s) models are the basis for extensive work in natural language processing.
A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal
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.
Examining the State-of-the-Art in News Timeline Summarization
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.
Revisiting the Centroid-based Method: A Strong Baseline for Multi-Document Summarization
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.
