natural-language-processing-11
Document Summarization
3260 papers • 126 benchmarks • 313 datasets
Automatic Document Summarization is the task of rewriting a document into its shorter form while still retaining its important content. The most popular two paradigms are extractive approaches and abstractive approaches. Extractive approaches generate summaries by extracting parts of the original document (usually sentences), while abstractive methods may generate new words or phrases which are not in the original document. Source: HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in document-summarization-11
Trend
Dataset
Best Model
Actions
CNN / Daily Mail
CNN / Daily Mail
HowSumm-Step
HowSumm-Step
HowSumm-Method
Libraries
iUse these libraries to find document-summarization-11 models and implementations
3 papers 134,285
Datasets
Subtasks
Most implemented papers
Get To The Point: Summarization with Pointer-Generator Networks
Peter J. Liu, Christopher D. Manning, A. See•Fri Mar 31 2017
A novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways, using a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator.
4307
