Current abstractive summarization models either suffer from a lack of clear interpretability or provide incomplete rationales by only highlighting parts of the source document. To this end, we propose the Summarization Program (SP), an interpretable modular framework consisting of an (ordered) list of binary trees, each encoding the step-by-step generative process of an abstractive summary sentence from the source document. A Summarization Program contains one root node per summary sentence, and a distinct tree connects each summary sentence (root node) to the document sentences (leaf nodes) from which it is derived, with the connecting nodes containing intermediate generated sentences. Edges represent different modular operations involved in summarization such as sentence fusion, compression, and paraphrasing. We first propose an efficient best-first search method over neural modules, SP-Search that identifies SPs for human summaries by directly optimizing for ROUGE scores. Next, using these programs as automatic supervision, we propose seq2seq models that generate Summarization Programs, which are then executed to obtain final summaries. We demonstrate that SP-Search effectively represents the generative process behind human summaries using modules that are typically faithful to their intended behavior. We also conduct a simulation study to show that Summarization Programs improve the interpretability of summarization models by allowing humans to better simulate model reasoning. Summarization Programs constitute a promising step toward interpretable and modular abstractive summarization, a complex task previously addressed primarily through blackbox end-to-end neural systems. Supporting code available at https://github.com/swarnaHub/SummarizationPrograms
翻译:当前抽象总和模型要么缺乏清晰的解释性,要么仅仅通过突出源文件的某些部分而提供不完全的理由。为此,我们建议采用“Summarization Programme”(SP),这是一个可解释的模块框架,由二进制树(顺序排列)列表组成,每个框架都编码了源文件抽象摘要句子的逐步基因化进程。一个“Summarization Programme”包含每个摘要句子的根节点,一个单独的树将每个摘要句子(根节点)与文件句子(leaf节点)连接起来,并连接含有中间生成的句子的节点连接起来。“Esummarization Programme” 代表了在组合、压缩和parphrasing等组合中涉及的不同的模块操作。我们首先提出一种有效的最佳搜索方法,即通过直接优化 ROUUGE的评分数来识别人类摘要的SP。接下来,我们提出产生“Summariceqeqequal化程序”的后,然后执行“Summarizalalalizalalalalizalizalizalizalizalizalizalizalizal化” exalizalizalization Supulation Supulation Supulation 。我们演示了一种在人类模拟程序之后,我们用“Sulizmalizmalutusalutalutalutalutal ” 。我们用了一个“Sulutalutusmalutalutalutaltaltalutalutaltalutalutal 。