Xsum Paper, This repository contains data and code for our EMN

Xsum Paper, This repository contains data and code for our EMNLP 2018 paper "Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization". Extreme Summarization (XSum) Dataset. List of papers on hallucination detection in LLMs. By conducting a human evaluation on ten Dataset Card for "xsum" Dataset Summary Extreme Summarization (XSum) Dataset. On CNN, SFT outperforms the more expensive methods. pdf at master · radfordneal/xsum "paper-bibtext": "```\n@InProceedings {xsum-emnlp,\n author = \"Shashi Narayan and Shay B. We leverage large language models Mirror of https://huggingface. Cohen and Mirella Lapata\",\n title = \"Don't Give Me the Details, Just the Summary! we update the BigPatent dataset to preserve casing, some format cleanings are also changed, please refer to change in TFDS. Large language models (LLMs) have shown promise for automatic summarization but the reasons behind their successes are poorly understood. The Query-Focused Text Summarization (QFTS) task aims at building systems that generate the summary of the text document(s) based on the given query. e. , BartForConditionalGeneration) while our model checkpoint on XSum We’re on a journey to advance and democratize artificial intelligence through open source and open science. For both BART and Pegasus, SFT produces distilled models that are 75% faster XSum is an English news summarization dataset where the task is to predict the first sentence of an article from the rest of it. context XSUM and CNN/DailyMail tasks. - EdinburghNLP/awesome-hallucination-detection The remainder of this paper is organized as follows. Our strategy of auto-matic annotation of summaries resembles XSum to some extent, but we found the first line to con-tain meta-information in many articles (e. We’re on a journey to advance and democratize artificial intelligence through open source and open science. We will use versions of this model fi uding standard summarization datasets. We conducted human evaluation studies to validate our experimental design and demonstrate human-level sum-marization performance on XSum, CNN/DailyMail, and Reddit TIFU. Section two provides an overview of the related work and compares them with our work. Leveraging Large Language Models (LLMs) has shown context learning on the XSum dataset. We first compute entity coverage precision and prepend the corresponding control Contribute to google-research/pegasus development by creating an account on GitHub. Paper To read more about XSum, see the paper that can be downloaded here. , au-thor information, date of last Exactly-rounded summation of floating point values - spsforks/radfordneal-xsum We’re on a journey to advance and democratize artificial intelligence through open source and open science. This provides a use-ful point of comparison between task-specific fine-tuned Exactly-rounded summation of floating point values - xsum/xsum-paper. All datasets are differ-ent, in terms of domain, inherent position bias s, or article and gold summary length. co Model. We test all three methods on the CNN and XSUM datasets. Many businesses and householders were affected by flooding in Newton Stewart after the River Cree xsum XSum is an English news summarization dataset where the task is to predict the first sentence of an article from the rest of it. We argue that the degradation comes from the short ORACLE of XSum, which brings more confusion with a few ORACLE examples. and Lapata, Mirella", title = "Don't Give Me the The XSum dataset consists of 226,711 Wayback archived BBC articles ranging over almost a decade (2010 to 2017) and covering a wide variety of domains This paper presents XSum, a modular pipeline for multi-document summarization (MDS) in the scientific domain using Retrieval-Augmented Generation (RAG). For finetuning details and scripts, see the The model creators note in the associated paper We attribute BRIO-Ctr’s superior performance to its use of the same model architecture (BART) for both candidate generation and scoring, while SimCLS Extreme Summarization (XSum) 数据集是用于评估抽象单文档摘要系统的数据集。目标是创建一个简短的、一句话的新摘要来回答“这篇文章是关于什么的？”这个问题。该数据集由 226,711 篇新闻文章组 For what it’s worth, the original ROUGE paper states that "ROUGE-2 and ROUGE-L worked well in single document summarization tasks" while "ROUGE-1 and ROUGE-L perform great in evaluating Tackling Text Summarization Hallucinations with Rejection Sampling in XSUM Dataset “Have you ever encountered the problem of models generating incorrect or misleading information in their To fill this gap, this paper provides researchers with a comprehensive survey of DL-based abstractive summarization. This suggests In this paper, we propose an innovative and interpretable contrastive learning based framework for extractive summarization called DCDSum, which comprises a D iverse Oracle evaluator, a C All datasets contain summaries generated from articles in CNN/DM and XSum.

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