Year: 2015
Type: conference-paper
Abstract: Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build.In this work, we propose a fully data-driven approach to abstractive sentence summarization.Our method utilizes a local attention-based model that generates each word of the summary conditioned on the input sentence.While the model is structurally simple, it can easily be trained end-to-end and scales to a large amount of training data.The model shows significant performance gains on the DUC-2004 shared task compared with several strong baselines.
DOI: 10.18653/v1/d15-1044
Authors: Alexander M. Rush, Sumit Chopra, Jason Weston
Institutions: Harvard University, Meta (United States), Imperial College London
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W1843891098