Document Summarization

56 papers with code · Natural Language Processing
Subtask of Text Summarization

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

Benchmarks

Greatest papers with code

Language Models are Unsupervised Multitask Learners

Preprint 2019 huggingface/transformers

Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets.

 Ranked #1 on Language Modelling on enwik8 (using extra training data)

COMMON SENSE REASONING DOCUMENT SUMMARIZATION LANGUAGE MODELLING MACHINE TRANSLATION QUESTION ANSWERING READING COMPREHENSION TEXT GENERATION

Generating Wikipedia by Summarizing Long Sequences

ICLR 2018 tensorflow/tensor2tensor

We show that generating English Wikipedia articles can be approached as a multi- document summarization of source documents.

DOCUMENT SUMMARIZATION MULTI-DOCUMENT SUMMARIZATION

Text Summarization with Pretrained Encoders

IJCNLP 2019 nlpyang/PreSumm

For abstractive summarization, we propose a new fine-tuning schedule which adopts different optimizers for the encoder and the decoder as a means of alleviating the mismatch between the two (the former is pretrained while the latter is not).

Ranked #3 on Document Summarization on CNN / Daily Mail (using extra training data)

ABSTRACTIVE TEXT SUMMARIZATION DOCUMENT SUMMARIZATION EXTRACTIVE DOCUMENT SUMMARIZATION

StructSum: Incorporating Latent and Explicit Sentence Dependencies for Single Document Summarization

1 Mar 2020atulkum/pointer_summarizer

Traditional preneural approaches to single document summarization relied on modeling the intermediate structure of a document before generating the summary.

DOCUMENT SUMMARIZATION

Ranking Sentences for Extractive Summarization with Reinforcement Learning

NAACL 2018 shashiongithub/Refresh

In this paper we conceptualize extractive summarization as a sentence ranking task and propose a novel training algorithm which globally optimizes the ROUGE evaluation metric through a reinforcement learning objective.

DOCUMENT SUMMARIZATION

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

EMNLP 2018 shashiongithub/XSum

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

DOCUMENT SUMMARIZATION

Extractive Summarization as Text Matching

ACL 2020 maszhongming/MatchSum

This paper creates a paradigm shift with regard to the way we build neural extractive summarization systems.

DOCUMENT SUMMARIZATION TEXT MATCHING

Overview and Results: CL-SciSumm Shared Task 2019

23 Jul 2019WING-NUS/scisumm-corpus

All papers are from the open access research papers in the CL domain.

DOCUMENT SUMMARIZATION INFORMATION RETRIEVAL