抽象式文本摘要综述：数据集、模型与评估指标
Survey on Abstractive Text Summarization: Dataset, Models, and Metrics
AI总结：
本综述探讨了基于深度学习的抽象式文本摘要技术，系统回顾了相关数据集、评估指标及基于transformer的模型，并通过测试用例分析了当代模型的优势与局限。
摘要：
深度学习的进步，特别是transformers的引入，在增强各种自然语言处理（NLP）任务方面发挥了关键作用。这些任务包括文本到文本的应用（如机器翻译、文本分类和文本摘要），数据到文本的任务（如回复生成），以及图像到文本的任务（如图像描述）。Transformer模型以其注意力机制、在通用知识上的预训练以及对下游任务的微调而著称。这带来了显著的改进，特别是在抽象式摘要中，通过对源文档的各个部分进行改写，生成与人类表达高度相似的摘要。这些模型的有效性通过多种指标进行评估，包括语义重叠和事实正确性等技术。本综述考察了文本摘要模型的最新技术水平，特别关注抽象式摘要方法。它回顾了用于衡量模型性能的各种数据集和评估指标。此外，它还包含了使用抽象式摘要模型的测试用例结果，以强调当代基于transformer的模型的优势与局限性。源代码和数据可在 https://github.com/gospelnnadi/Text-Summarization-SOTA-Experiment 获取。
英文摘要：
The advancements in deep learning, particularly the introduction of transformers, have been pivotal in enhancing various natural language processing (NLP) tasks. These include text-to-text applications such as machine translation, text classification, and text summarization, as well as data-to-text tasks like response generation and image-to-text tasks such as captioning. Transformer models are distinguished by their attention mechanisms, pretraining on general knowledge, and fine-tuning for downstream tasks. This has led to significant improvements, particularly in abstractive summarization, where sections of a source document are paraphrased to produce summaries that closely resemble human expression. The effectiveness of these models is assessed using diverse metrics, encompassing techniques like semantic overlap and factual correctness. This survey examines the state of the art in text summarization models, with a specific focus on the abstractive summarization approach. It reviews various datasets and evaluation metrics used to measure model performance. Additionally, it includes the results of test cases using abstractive summarization models to underscore the advantages and limitations of contemporary transformer-based models. The source codes and the data are available at https://github.com/gospelnnadi/Text-Summarization-SOTA-Experiment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
