# Extreme Summarization

7 papers with code • 3 benchmarks • 3 datasets

# ByT5: Towards a token-free future with pre-trained byte-to-byte models

28 May 2021

In this paper, we show that a standard Transformer architecture can be used with minimal modifications to process byte sequences.

Ranked #1 on Question Answering on TweetQA (ROUGE-L metric)

54,730

# TLDR: Extreme Summarization of Scientific Documents

We introduce TLDR generation, a new form of extreme summarization, for scientific papers.

592

19 Jul 2019

We introduce 'extreme summarization', a new single-document summarization task which aims at creating a short, one-sentence news summary answering the question What is the article about?''.

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# Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization

We introduce extreme summarization, a new single-document summarization task which does not favor extractive strategies and calls for an abstractive modeling approach.

240

# Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles

Multi-document summarization is a challenging task for which there exists little large-scale datasets.

21

# A Convolutional Attention Network for Extreme Summarization of Source Code

9 Feb 2016

Attention mechanisms in neural networks have proved useful for problems in which the input and output do not have fixed dimension.

12

# TLDR9+: A Large Scale Resource for Extreme Summarization of Social Media Posts

Recent models in developing summarization systems consist of millions of parameters and the model performance is highly dependent on the abundance of training data.

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