Text Summarization
369 papers with code • 33 benchmarks • 87 datasets
Text Summarization is a natural language processing (NLP) task that involves condensing a lengthy text document into a shorter, more compact version while still retaining the most important information and meaning. The goal is to produce a summary that accurately represents the content of the original text in a concise form.
There are different approaches to text summarization, including extractive methods that identify and extract important sentences or phrases from the text, and abstractive methods that generate new text based on the content of the original text.
Benchmarks
These leaderboards are used to track progress in Text Summarization
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Libraries
Use these libraries to find Text Summarization models and implementationsDatasets
Pubmed
Reddit
CNN/Daily Mail
New York Times Annotated Corpus
CORD-19
S2ORC
WikiHow
Multi-News
NEWSROOM
Subtasks
Latest papers
The Radiation Oncology NLP Database
ROND is specifically designed to address this gap in the domain of radiation oncology, a field that offers many opportunities for NLP exploration.
Hyperparameter-Free Approach for Faster Minimum Bayes Risk Decoding
CyberAgentAILab/adaptive-mbr
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Minimum Bayes-Risk (MBR) decoding is shown to be a powerful alternative to beam search decoding for a wide range of text generation tasks.
Lookahead: An Inference Acceleration Framework for Large Language Model with Lossless Generation Accuracy
alipay/PainlessInferenceAcceleration
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Hence, this paper presents a generic framework for accelerating the inference process, resulting in a substantial increase in speed and cost reduction for our RAG system, with lossless generation accuracy.
Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation
This study introduces Ascle, a pioneering natural language processing (NLP) toolkit designed for medical text generation.
Exploring Prompting Large Language Models as Explainable Metrics
ghazaleh-mahmoodi/Prompting_LLMs_AS_Explainable_Metrics
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This paper describes the IUST NLP Lab submission to the Prompting Large Language Models as Explainable Metrics Shared Task at the Eval4NLP 2023 Workshop on Evaluation & Comparison of NLP Systems.
DynaPipe: Optimizing Multi-task Training through Dynamic Pipelines
awslabs/optimizing-multitask-training-through-dynamic-pipelines
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This paper proposes a dynamic micro-batching approach to tackle sequence length variation and enable efficient multi-task model training.
Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization
Our study reveals that instruction controllable text summarization remains a challenging task for LLMs, since (1) all LLMs evaluated still make factual and other types of errors in their summaries; (2) all LLM-based evaluation methods cannot achieve a strong alignment with human annotators when judging the quality of candidate summaries; (3) different LLMs show large performance gaps in summary generation and evaluation.
Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects -- A Survey
Generic text summarization approaches often fail to address the specific intent and needs of individual users.
GreekT5: A Series of Greek Sequence-to-Sequence Models for News Summarization
The proposed models were thoroughly evaluated on the same dataset against GreekBART, which is the state-of-the-art model in Greek abstractive news summarization.
Boosting Summarization with Normalizing Flows and Aggressive Training
This paper presents FlowSUM, a normalizing flows-based variational encoder-decoder framework for Transformer-based summarization.
