Expository Text Generation: Imitate, Retrieve, Paraphrase (2023)

Abstract: Expository documents are vital resources for conveying complex information to readers. Despite their usefulness, writing expository text by hand is a challenging process that requires careful content planning, obtaining facts from multiple sources, and the ability to clearly synthesize these facts. To ease these burdens, we propose the task of expository text generation, which seeks to automatically generate an accurate and stylistically consistent expository text for a topic by intelligently searching a knowledge source. We solve our task by developing IRP, a framework that overcomes the limitations of retrieval-augmented models and iteratively performs content planning, fact retrieval, and rephrasing. Through experiments on three diverse, newly-collected datasets, we show that IRP produces factual and organized expository texts that accurately inform readers.

## introduction expository writing intends to inform a reader about a topic in a clear and logical manner @xcite . such text is highly prevalent online, appearing in various forms such as university descriptions, medical information, and wikipedia articles. despite its importance, writing expository text is a difficult task, requiring the author to carefully plan content for the text, obtain facts from multiple sources, and rephrase the facts so the text flows smoothly @xcite @xcite . although it requires effort, expository writing is vital for making complex information accessible to readers. to ease these burdens, we propose the task of expository text generation. as seen in figure 1 , the task uses an input title or topic (e.g. college name) and a knowledge source of topic-related factual sentences to create a multi-sentence expository text 1 code is available at https://github.com/ nbalepur/expository-text-generation . ## method the inputs for expository text generation include 1) a topic t for the expository text and 2) a corpus of factual sentences @xmath0. we fix the corpus c to compare models, but in practice, c can be acquired in real time for up-to-date facts. to guide the initial generation, irp also uses 3) a sequence of words @xmath1. using these inputs, irp aims to produce a sequence of sentences @xmath2. the text d must contain accurate facts about t from c, and must be presented in a consistent style dictated by expository texts in the training set @xmath3, ..., d n }. as illustrated in figure 2 , irp leverages three components: 1) a style imitator p(y i |y 1:i-1 ) that generates a stylistic content plan y i for the next sentence in the expository document, based on the current state of the output y 1:i-1 (or prefix r in the first iteration); 2) a retriever p(x j |y i ) that returns the top-k factual sentences x ⊆ c most related to the content plan y i ; and 3) a paraphraser p(z|x, y i ) that combines the semantics of x and the syntax of y i into a reworded sentence z. we will describe each of these modules, followed by how they are combined and trained for the full irp model. ## conclusion we introduce the task of expository text generation and design irp to overcome the limitations of existing models. irp explicitly and iteratively performs content planning, fact selection, and paraphrasing to generate high quality expository texts. automatic and human evaluations on three newlycollected datasets reveal that irp preserves factual accuracy while maintaining stylistic consistency. our ablation studies confirm the importance of sentence-level generation and creating fine-grained search queries. finally, we visualize the retriever of irp and study the factual errors produced by our model to suggest future research directions.