# Summarization

3 papers with code • 24 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.

# Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond

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.

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# Sparsifying Transformer Models with Trainable Representation Pooling

A reduction of quadratic time and memory complexity to sublinear was achieved due to a robust trainable top-$k$ operator.

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# MuLD: The Multitask Long Document Benchmark

15 Feb 2022

The impressive progress in NLP techniques has been driven by the development of multi-task benchmarks such as GLUE and SuperGLUE.

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