Summarization
3 papers with code • 7 benchmarks • 6 datasets
Summarization is the task of producing a shorter version of one or several documents that preserves most of the input's meaning.
Most implemented papers
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.
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.
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.