RA-MTR：一种基于检索增强多任务阅读器的长文档励志语录提取方法
RA-MTR: A Retrieval Augmented Multi-Task Reader based Approach for Inspirational Quote Extraction from Long Documents
AI总结：
本文将长文档励志语录提取建模为开放域问答，提出结合向量检索器与多任务阅读器的 RA-MTR 框架，并构建三个数据集，显著提升 BoW F1。
摘要：
名人的励志语录常被用于新闻文章、散文和日常对话中以传达思想。本文提出一种新颖的基于上下文的语录提取系统，旨在从长文本中提取最相关的语录。我们首先将该语录提取任务建模为开放域问答问题，采用基于向量存储的检索器，随后应用多任务阅读器。我们构建了三个基于上下文的语录提取数据集，并引入新颖的多任务框架 RA-MTR；该框架提升了当前最优性能，在 BoW F1 分数上实现最高 5.08% 的改进。
英文摘要：
Inspirational quotes from famous individuals are often used to convey thoughts in news articles, essays, and everyday conversations. In this paper, we propose a novel context-based quote extraction system that aims to extract the most relevant quote from a long text. We formulate this quote extraction as an open domain question answering problem first by employing a vector-store based retriever and then applying a multi-task reader. We curate three context-based quote extraction datasets and introduce a novel multi-task framework RA-MTR that improves the state-of-the-art performance, achieving a maximum improvement of 5.08% in BoW F1-score.
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。
