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

信息充斥着人们的日常生活，而且势不可挡。识别重要信息并简洁呈现的摘要系统可以提供帮助。要使其在现实场景中可用，文本摘要必须具备的最重要的一个特征是其可靠性。如果可以相信摘要的内容与原始内容保持准确，则该摘要是可靠的。虽然深层神经架构在抽象概括方面取得了成功，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幻觉内容，从而改变原始文本的含义。抽象摘要系统旨在利用自然语言生成功能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看不到的新单词和短语。随着词汇选择的灵活性的提高，对可靠性的要求也随之提高——摘要必须完整地保留原文的含义。如果不强调总体可靠性，系统输出往好里说可能毫无用处，往坏里说可能会产生误导和有害。因此，迫切需要开发强大的文本摘要器，其输出可以保留原文的含义。该项目将对科学技术和社会发展产生重大影响。在该项目中获得的知识可以扩展，以帮助构建对机器翻译至关重要的强大的语言生成能力。该项目将为本科生和研究生提供资助，本科生与研究生合作，获得实践经验并促进指导。该项目旨在建立强大的抽象摘要系统，通过利用深度神经模型和语言结构预测的力量，其摘要可以保持原始文本的真实性。鉴于摘要的主要关系（例如，谁对谁做了什么）通常与源文本的关系相同或相似，该项目的重点是开发方法，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，从而防止摘要极大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、句子到句子的摘要器，联合执行摘要句子的生成和句子结构的解析。 (b) 开发一个多对一的句子摘要器，明确模拟源文本中观察到的提及之间的共指关系。利用深度神经架构的最新发展，这些努力预计将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估方案，利用问答来评估系统摘要在多大程度上保留了原始文本的含义。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力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.