使用前沿 NLP Transformer 和 LSTM 的简历抽象文本摘要
Abstractive Text Summarization for Resumes With Cutting Edge NLP Transformers and LSTM
AI总结：
本研究评估多种NLP模型在简历摘要任务上的性能，发现微调的BART-Large模型表现最佳。
摘要：
文本摘要是自然语言处理中的一项基本任务，旨在将大量文本信息压缩为简洁连贯的摘要。随着内容的指数级增长以及高效提取关键信息的需求，近年来文本摘要受到了广泛关注。在本研究中，评估了 LSTM 以及预训练的 T5、Pegasus、BART 和 BART-Large 模型在开源数据集（Xsum、CNN/Daily Mail、Amazon Fine Food Review 和 News Summary）以及准备好的简历数据集上的性能。该简历数据集包含语言、教育、经验、个人信息、技能等许多信息，该数据包含 75 份简历。本研究的主要目标是对简历文本进行分类。使用简历数据集评估了各种技术，如 LSTM、预训练模型和微调模型。使用简历数据集进行微调的 BART-Large 模型表现最佳。
英文摘要：
Text summarization is a fundamental task in natural language processing that aims to condense large amounts of textual information into concise and coherent summaries. With the exponential growth of content and the need to extract key information efficiently, text summarization has gained significant attention in recent years. In this study, LSTM and pre-trained T5, Pegasus, BART and BART-Large model performances were evaluated on the open source dataset (Xsum, CNN/Daily Mail, Amazon Fine Food Review and News Summary) and the prepared resume dataset. This resume dataset consists of many information such as language, education, experience, personal information, skills, and this data includes 75 resumes. The primary objective of this research was to classify resume text. Various techniques such as LSTM, pre-trained models, and fine-tuned models were assessed using a dataset of resumes. The BART-Large model fine-tuned with the resume dataset gave the best performance.
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。
