Summarization
3 papers with code • 25 benchmarks • 9 datasets
Summarization is the task of producing a shorter version of one or several documents that preserves most of the input's meaning.
Benchmarks
These leaderboards are used to track progress in Summarization
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
MNIST
CNN/Daily Mail
SAMSum
Multi-News
BillSum
XSum
MuLD
Most implemented papers
Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
theamrzaki/text_summurization_abstractive_methods
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CONLL 2016
In this work, we model abstractive text summarization using Attentional Encoder-Decoder Recurrent Neural Networks, and show that they achieve state-of-the-art performance on two different corpora.
Sparsifying Transformer Models with Trainable Representation Pooling
applicaai/pyramidions
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ACL 2022
A reduction of quadratic time and memory complexity to sublinear was achieved due to a robust trainable top-$k$ operator.
MuLD: The Multitask Long Document Benchmark
The impressive progress in NLP techniques has been driven by the development of multi-task benchmarks such as GLUE and SuperGLUE.
