Summarizing Procedural Text: Data and Approach (2022)

Abstract: Procedural text is a widely used genre that contains many steps of instructions of how to cook a dish or how to conduct a chemical experiment and analyze the procedural text has become a popular task in the NLP field. Since the procedural text can be very long and contains many details, summarizing the whole procedural text or giving an overview for each complicated procedure step can save time for readers and help them to capture the core information in the text. In this paper, we propose the procedural text summarization task with two summarization granularity: step-view and global-view, which summarizes each step in the procedural text separately or gives an overall summary for all steps respectively. To tackle this task, we propose an Entity-State Graph-based Summarizer (ESGS) which is based on state-of-the-art entity state tracking methods and constructs a heterogeneous graph to aggregate contextual information for each procedure. In order to help the summarization model focus on the salient entities, we propose to use the contextualized procedure graph representation to predict the salient entities. Experiments conducted on two datasets verify the effectiveness of our proposed model. Our code and datasets will be released on https://github.com/gsh199449/procedural-summ.

## introduction procedural texts, e.g., scientific articles, instruction books, or recipes, are widely spread and useful in many real-world applications @xcite @xcite . in procedural text modeling field, many research works focus on entity state tracking @xcite @xcite and reasoning @xcite , and how to summarize the procedural text has not been fully * equal contribution. ## problem formulation given a procedural text @xmath0, • • • , s lp } with l p procedures and each procedure s @xmath1: wash the potatoes... s2: cut the chicken breast... s3: take a piece of butter... 1 2 3 1 2 3 1 2 3 1 2 3 entity selection entity selection cross-attention feed forward output embedding decoder kl divergence ground truth summary bow vector bow loss subgraph of metapath #1 subgraph of metapath #2 heterogeneous graph prior distribution posterior distribution node-level attention state-aware nodelevel attention semantic-level attention metapath #1 metapath #2 2 bart potatoes potatoes 2 marinate chicken with potato slices and refrigerate 2 hours chicken generated summary procedures procedure entity ly }, where ŷ i is for procedure s i and it has l y is the number of words of the summary. and in the global-view, we aim to generate a summary for all the procedures in p . finally, we use the difference between generated summary and the ground truth as the training objective. in the following sections, we use the step-view as the example to illustrate our method, and the model difference for global-view is introduced in § 4.7. ## conclusion in this paper, we propose the procedural text summarization task which aims to generate two granularity summaries @xcite . we propose to use the heterogeneous graph model entity-state graph-based summarizer (esgs) to construct the relationship between procedures using the trace of entity state changes. to focus on the salient entities, we also propose an entity selection module that is trained by using posterior information to provide effective guidance. finally, we generate the summary by incorporating the updated graph and selected salient entities. and we conduct experiments on a modified version of benchmark dataset wikihow proc , and we also construct a new procedural text summarization dataset psystory with global-view summary. esgs achieves the state-of-the-art performance on both datasets.