Extract, Select and Rewrite: A Modular Sentence Summarization Method (2023)

Abstract: A modular approach has the advantage of being compositional and controllable, comparing to most end-to-end models. In this paper we propose Extract-Select-Rewrite (ESR), a three-phase abstractive sentence summarization method. We decompose summarization into three stages: (i) knowledge extraction, where we extract relation triples from the text using off-the-shelf tools; (ii) content selection, where a subset of triples are selected; and (iii) rewriting, where the selected triple are realized into natural language. Our results demonstrates that ESR is competitive with the best end-to-end models while being more faithful. %than these baseline models. Being modular, ESR’s modules can be trained on separate data which is beneficial in low-resource settings and enhancing the style controllability on text generation.

## introduction while end-to-end models are dominating text generation tasks today, modular or pipelined approaches have the advantage of greater controllability and interpretability @xcite . prior work on abstractive summarization adopts a twostep process of first generating a plan (e.g., a semantic representation) of the target summary and then generating the summary conditioned on both the plan and the input document @xcite . in this paper, we present a threephase extract-select-rewrite pipeline, or esr, for abstractive sentence summarization, where the plan is restricted to be a subset of knowledge triples extracted from the document. specifically, we decompose the task into three subtasks: knowledge extraction, content selection and rewriting. to implement the three modules, we extract knowledge triples from the source document using off-the-shelf tools. then, we train a classifier to select important triples representing content of the summary. finally, we train a rewriter to convert the selected triples into natural language text (figure 1 ). there is extensive prior work that uses structured content extracted from the document to help summarization, such as relation triples @xcite , knowledge graphs @xcite , and topics @xcite @xcite . however, these methods typically augment the source document with the extracted information and still learn to generate reference summaries from it in an end-to-end manner. by fully separating the modules during training, we can take a rewriter trained on a large dataset, and reuse it on a small target dataset while only training the content selector on as few as 1k examples. we run experiments on gigaword, duc-2004 and reddit-tifu datasets and find that our approach produces summaries that are competitive to the end-to-end models in terms of automatic metrics. we also observe that a rewriter module trained on gigaword, in the news domain, can be paired with a content selector trained on 1000 examples from reddit-tifu, a social media dataset, to produce high quality summaries, demonstrating the value of modularity in abstractive summarization. further, since our content planning is extractive in nature the summaries generated are also more faithful to the source as evidenced by a human evaluation comparing summaries from our modular approach and an end-to-end bart baseline. lastly, we also observe that the rewriter module can be trained once on standalone text, which can enhance the controllability on the summary text generation style with minor changes of the training process. ## method we divide the summarization task explicitly into three phases-knowledge extraction, content selection, and rewriting, as shown in figure 1 . knowledge extraction to enable fine-grained content selection and rewriting, we turn all documents into structured content representation. we adopt knowledge triples that can be extracted by off-the-shelf tools (section 4.1). the knowledge triples are in the form @xmath0, relation, entity 2>. the extractors usually generate a large number of redundant triples (i.e. triples with large overlap with each other. 2 to 2 for example, given the sentence "german chemical giant hoechst group announced plans wednesday to invest over a million dollars in china next year" our extractors might generate two @xmath1, we use the jaccard distance on n-grams (j u ni , j bi ) of between any pairs of triples (x 1 , x 2 ) to calculate their similarity: here λ 1 , λ 2 are hyperparameters determined on the validation data. we filter triples such that no pair of triples has a similarity score higher than the threshold. if the similarity between two tripes are larger than the threshold, the triple that has the larger length will be kept. the details of the threshold are in section 4.1. ## conclusion we propose esr, a three-phase modular abstractive summarization method. it obtains competitive performance on automatic metrics while producing more faithful summaries, and its modularity makes it have a good controllability on summary generation, and maintains a good performance on low resource data. in the future, we are adapting the esr method to multi-document summarization datasets.