RI: Small: Towards Abstractive Summarization That Preserves the Original Meaning

信息充斥着人们的日常生活，势不可挡。能够识别重要信息并将其简洁地呈现出来的总结系统会有所帮助。要使文本摘要在实际场景中可用，它必须具备的最重要的一个特征是它的可靠性。如果摘要的内容与原文保持一致，那么它就是可靠的。虽然深度神经架构在抽象摘要方面已经取得了成功，但研究表明，系统生成的摘要可能包含不准确的事实细节或改变原始文本含义的幻觉内容。抽象摘要系统寻求使用自然语言生成能力将冗长的源文本转换为简洁的摘要；摘要可以包含源输入中未见的新单词和短语。随着词汇选择的灵活性越来越大，对可靠性的要求也越来越高——摘要必须保持原文的意思不变。如果不强调总结的可靠性，系统输出往好里说是无用的，往坏里说是误导和有害的。因此，存在着迫切的需求，本项目旨在开发健壮的文本摘要器，其输出可以保留原文的含义。这个项目将对科学技术和社会发展产生重大影响。在这个项目中获得的知识可以扩展到帮助构建健壮的语言生成能力，这对机器翻译至关重要。该项目将资助本科生和研究生，本科生与研究生组成团队，以获得实践经验并促进师友关系。本项目旨在通过利用深度神经模型和语言结构预测的力量，构建健壮的抽象摘要系统，其摘要可以保持对原始文本的真实性。鉴于摘要的主要关系（例如，谁对谁做了什么）通常与源文本相同或相似，该项目侧重于开发方法，以学习促进保留重要源关系的摘要，并阻止包含错误关系的摘要，从而防止摘要戏剧性地改变原始文本的含义。研究目标包括以下内容。(a)开发一个抽象的句子对句子的总结器，它可以共同生成总结句和分析句子结构。(b)开发一个多对一的句子摘要器，明确地模拟源文本中观察到的提及之间的相互引用关系。根据深度神经架构的最新发展，这些努力有望改进神经抽象多文档摘要器，以帮助其正确编码源文本并解码摘要序列。(c)设计一种新颖的半自动评价办法，利用问答来评价系统摘要在多大程度上保留了原文的意思。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Information floods people's daily lives, and it is overwhelming. Summarization systems that identify salient pieces of information and present it concisely can help. The single most important characteristic a text summary must possess to make it usable in real-world scenarios is its reliability. A summary is reliable if its content can be trusted to remain accurate to the original. While deep neural architectures have demonstrated success in abstractive summarization, studies reveal that system-generated abstracts can contain inaccurate factual details or hallucinated content that change the meaning of the original texts. An abstractive summarization system seeks to transform lengthy source texts to a succinct summary using natural language generation capabilities; the summary can contain new words and phrases that are unseen in the source input. With greater flexibility of lexical choices comes increased demand for reliability---summaries must keep the meaning of the original intact. Without emphasizing summary reliability, system outputs can render useless at best, and misleading and detrimental at worst. Thus, there exists a pressing need, and this project aims to develop robust text summarizers whose outputs can preserve the meaning of the original. This project will have major impact on science and technology as well as the development of society. The knowledge acquired in this project can be extended to help build robust language generation capabilities that are crucial for machine translation. This project will fund both undergraduate and graduate students where undergraduate students are teamed up with graduate students to gain hands-on experiences and promote mentorship.This project aims to build robust abstractive summarization systems whose summaries can remain true to the original texts by harnessing the power of deep neural models and linguistic structure prediction. Given that major relations of a summary (e.g., who did what to whom) are often the same or similar to those of the source text, the project focuses on developing methods that learn to promote summaries that preserve important source relations and discourage summaries that contain erroneous relations, thus preventing a summary from dramatically changing the meaning of the original text. The research objective includes the following. (a) Developing an abstractive, sentence-to-sentence summarizer that jointly performs generation of summary sentences and parsing sentence structures. (b) Developing a many-to-one sentence summarizer that explicitly models coreference relationships between mentions observed in the source text. Drawing upon recent developments in deep neural architectures, these efforts are expected to improve a neural abstractive multi-document summarizer to help it properly encode the source texts and decode the summary sequence. (c) Devising a novel, semi-automatic evaluation scheme leveraging question-answering to assess to what extent system summaries preserve the meaning of the original texts.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.