F act-based T ext E diting (2020)

Abstract: We propose a novel text editing task, referred to as fact-based text editing , in which the goal is to revise a given document to better describe the facts in a knowledge base (e.g., several triples). The task is important in practice because reflecting the truth is a common requirement in text editing. First, we propose a method for automatically generating a dataset for research on fact-based text editing, where each instance consists of a draft text, a revised text, and several facts represented in triples. We apply the method into two public table-to-text datasets, obtaining two new datasets consisting of 233k and 37k instances, respectively. Next, we propose a new neural network architecture for fact-based text editing, called FactEditor, which edits a draft text by referring to given facts using a buffer, a stream, and a memory. A straightforward approach to address the problem would be to employ an encoder-decoder model. Our experimental results on the two datasets show that FactEditor outperforms the encoder-decoder approach in terms of fidelity and fluency. The results also show that FactEditor conducts inference faster than the encoder-decoder approach.

## introduction automatic editing of text by computer is an important application, which can help human writers to write better documents in terms of accuracy, fluency, etc. the task is easier and more practical than the automatic generation of texts from scratch and is attracting attention recently @xcite . in this paper, we consider a new and specific setting of it, referred to as fact-based text editing, in which a draft text and several facts (represented in triples) are given, and the system set of triples {(baymax, creator, douncan rouleau), (douncan rouleau, nationality, american), (baymax, creator, steven t. seagle), (steven t. seagle, nationality, american), (baymax, series, big hero 6), (big hero 6, starring, scott adsit)} draft text baymax was created by duncan rouleau, a winner of eagle award. baymax is a character in big hero 6 . revised text baymax was created by american creators duncan rouleau and steven t. seagle . baymax is a character in big hero 6 which stars scott adsit . as far as we know, no previous work did address the problem. in a text-to-text generation, given a text, the system automatically creates another text, where the new text can be a text in another language (machine translation), a summary of the original text (summarization), or a text in better form (text editing). in a table-to-text generation, given a table containing facts in triples, the system automatically composes a text, which describes the facts. the former is a text-to-text problem, and the latter a table-to-text problem. in comparison, fact-based text editing can be viewed as a 'text&table-to-text' problem. first, we devise a method for automatically creating a dataset for fact-based text editing. recently, several table-to-text datasets have been created and released, consisting of pairs of facts and corresponding descriptions. we leverage such kind of data in our method. we first retrieve facts and their descriptions. next, we take the descriptions as revised texts and automatically generate draft texts based on the facts using several rules. we build two datasets for fact-based text editing on the basis of webnlg @xcite and rotowire, consisting of 233k and 37k instances respectively @xcite foot_0 . second, we propose a model for fact-based text editing called facteditor. one could employ an encoder-decoder model, such as an encoderdecoder model, to perform the task. the encoderdecoder model implicitly represents the actions for transforming the draft text into a revised text. in contrast, facteditor explicitly represents the actions for text editing, including keep, drop, and gen, which means retention, deletion, and generation of word respectively. the model utilizes a buffer for storing the draft text, a stream to store the revised text, and a memory for storing the facts. it also employs a neural network to control the entire editing process. facteditor has a lower time complexity than the encoder-decoder model, and thus it can edit a text more efficiently. experimental results show that facteditor outperforms the baseline model of using encoderdecoder for text editing in terms of fidelity and fluency, and also show that facteditor can perform text editing faster than the encoder-decoder model. ## data creation in this section, we describe our method of data creation for fact-based text editing. the method automatically constructs a dataset from an existing table -to-text dataset. ## facteditor in this section, we describe our proposed model for fact-based text editing referred to as facteditor. ## experiment we conduct experiments to make comparison between facteditor and the baselines using the two datasets webedit and rotoedit. ## conclusion in this paper, we have defined a new task referred to as fact-based text editing and made two contributions to research on the problem. first, we have proposed a data construction method for fact-based text editing and created two datasets. second, we have proposed a model for fact-based text editing, named facteditor, which performs the task by generating a sequence of actions. experimental results show that the proposed model factedi-tor performs better and faster than the baselines, including an encoder-decoder model.