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.
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Latest papers
On the Benefits of Fine-Grained Loss Truncation: A Case Study on Factuality in Summarization
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.
German also Hallucinates! Inconsistency Detection in News Summaries with the Absinth Dataset
The advent of Large Language Models (LLMs) has led to remarkable progress on a wide range of natural language processing tasks.
Attribute Structuring Improves LLM-Based Evaluation of Clinical Text Summaries
Summarizing clinical text is crucial in health decision-support and clinical research.
TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization
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.
BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation
Large language models (LLMs) have demonstrated outstanding performance in various tasks, such as text summarization, text question-answering, and etc.
TL;DR Progress: Multi-faceted Literature Exploration in Text Summarization
This paper presents TL;DR Progress, a new tool for exploring the literature on neural text summarization.
A Survey of Large Language Models in Finance (FinLLMs)
This survey provides a comprehensive overview of FinLLMs, including their history, techniques, performance, and opportunities and challenges.
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.
Lookahead: An Inference Acceleration Framework for Large Language Model with Lossless Generation Accuracy
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.