调查企业社会责任倡议：以企业 Covid-19 响应为例
Investigating Corporate Social Responsibility Initiatives: Examining the case of corporate Covid-19 response
AI总结：
本文以企业 Covid-19 响应为例，应用 LDA、Doc2Vec、文本摘要及句子排序等自然语言处理方法分析企业新闻稿，帮助政策制定者从海量信息中提取要旨，为危机中的企业社会责任监管提供可复制的决策支持方法。
摘要：
在当今信息自由获取的时代，政策制定者在做出影响相关利益方的决策时，必须考虑海量信息。尽管信息来源和文档数量的增加提高了基于可用文本语料所做决策的可信度，但政策制定者理解这些信息仍面临挑战。本文展示了政策制定者如何实施一些最流行的主题识别方法，包括 Latent Dirichlet Allocation、Deep Distributed Representation 方法、文本摘要方法、Word Based Sentence Ranking 方法以及用于句子抽取的 TextRank 方法，以总结大量文档的内容，从而理解信息过载的要旨。我们将流行的自然语言处理方法应用于 Covid-19 疫情早期和后期阶段的企业新闻稿，这场疫情引发了全球前所未有的健康和社会经济危机，在此背景下，政策制定和监管对于规范企业实践、保障员工和社会福利以应对未来类似的不可预见危机变得尤为重要。本研究采取的步骤可以复制，以在任何其他社会决策情境中从相关文档中获取洞见。
英文摘要：
In todays age of freely available information, policy makers have to take into account a huge amount of information while making decisions affecting relevant stakeholders. While increase in the amount of information sources and documents increases credibility of decisions based on the corpus of available text, it is challenging for policymakers to make sense of this information. This paper demonstrates how policy makers can implement some of the most popular topic recognition methods, Latent Dirichlet Allocation, Deep Distributed Representation method, text summarization approaches, Word Based Sentence Ranking method and TextRank for sentence extraction method, to sum up the content of large volume of documents to understand the gist of the overload of information. We have applied popular NLP methods to corporate press releases during the early period and advanced period of Covid-19 pandemic which has resulted in a global unprecedented health and socio-economic crisis, when policymaking and regulations have become especially important to standardize corporate practices for employee and social welfare in the face of similar future unseen crises. The steps undertaken in this study can be replicated to yield insights from relevant documents in any other social decision-making context.
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。
