Text Summarization

362 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.

Libraries

Use these libraries to find Text Summarization models and implementations

On the Benefits of Fine-Grained Loss Truncation: A Case Study on Factuality in Summarization

yale-nlp/fine-grained-lt 9 Mar 2024

We study the behavior of the underlying losses between factual and non-factual examples, to understand and refine the performance of LT. We demonstrate that LT's performance is limited when the underlying assumption that noisy targets have higher NLL loss is not satisfied, and find that word-level NLL among entities provides better signal for distinguishing factuality.

0
09 Mar 2024

German also Hallucinates! Inconsistency Detection in News Summaries with the Absinth Dataset

mediatechnologycenter/absinth 6 Mar 2024

The advent of Large Language Models (LLMs) has led to remarkable progress on a wide range of natural language processing tasks.

1
06 Mar 2024

Attribute Structuring Improves LLM-Based Evaluation of Clinical Text Summaries

microsoft/attribute-structuring 1 Mar 2024

Summarizing clinical text is crucial in health decision-support and clinical research.

3
01 Mar 2024

TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization

amazon-science/tofueval 20 Feb 2024

We find that there are diverse errors and error distributions in model-generated summaries and that non-LLM based metrics can capture all error types better than LLM-based evaluators.

10
20 Feb 2024

BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation

opengvlab/llmprune-besa 18 Feb 2024

Large language models (LLMs) have demonstrated outstanding performance in various tasks, such as text summarization, text question-answering, and etc.

8
18 Feb 2024

TL;DR Progress: Multi-faceted Literature Exploration in Text Summarization

webis-de/eacl24-tldr-progress 10 Feb 2024

This paper presents TL;DR Progress, a new tool for exploring the literature on neural text summarization.

0
10 Feb 2024

A Survey of Large Language Models in Finance (FinLLMs)

adlnlp/finllms 4 Feb 2024

This survey provides a comprehensive overview of FinLLMs, including their history, techniques, performance, and opportunities and challenges.

40
04 Feb 2024

The Radiation Oncology NLP Database

zl-liu/radiation-oncology-nlp-database 19 Jan 2024

ROND is specifically designed to address this gap in the domain of radiation oncology, a field that offers many opportunities for NLP exploration.

8
19 Jan 2024

Lookahead: An Inference Acceleration Framework for Large Language Model with Lossless Generation Accuracy

alipay/PainlessInferenceAcceleration 20 Dec 2023

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.

223
20 Dec 2023

Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation

yale-lily/medgen 28 Nov 2023

This study introduces Ascle, a pioneering natural language processing (NLP) toolkit designed for medical text generation.

59
28 Nov 2023