Compositional Data Augmentation for Abstractive Conversation Summarization (2023)

Abstract: Recent abstractive conversation summarization systems generally rely on large-scale datasets with annotated summaries. However, collecting and annotating these conversations can be a time-consuming and labor-intensive task. To address this issue, in this work, we present a sub-structure level compositional data augmentation method, Compo, for generating diverse and high-quality pairs of conversations and summaries. Specifically, Compo first extracts conversation structures like topic splits and action triples as basic units. Then we organize these semantically meaningful conversation snippets compositionally to create new training instances. Additionally, we explore noise-tolerant settings in both self-training and joint-training paradigms to make the most of these augmented samples. Our experiments on benchmark datasets, SAMSum and DialogSum, show that Compo substantially outperforms prior baseline methods by achieving a nearly 10% increase of ROUGE scores with limited data. Code is available at https://github.com/ozyyshr/Compo .

## introduction abstractive conversation summarization, which condenses unstructured conversations into short, concise, and structured text, has greatly benefited from neural generative models trained on largescale annotated data. researchers have focused on various aspects in conversation summarization, such as hierarchical modeling of conversations @xcite , leveraging dialogue acts @xcite , using key phrases and entities @xcite , utilizing topic segments @xcite , incorporating stage components @xcite and examining discourse relations @xcite . however, training these generative models often requires abundant high-figure 1 : an example of conversation, extracted actions and its paired summary sentences (randomly sampled from samsum). the corresponding summary consists of three sentences, each sentence relates to one snippet (illustrated by color). a direct solution is to employ data augmentation (da) @xcite @xcite @xcite @xcite to generate more data. whereas, directly applying these augmentation methods into the context of conversations usually fails to consider any unique structures of conversations such as speaker information, topic split, and conversation stages @xcite , which distinguish conversations from general sentences. as a result, they might be limited in creating high-quality and diverse data pairs @xcite . even though there are a few exceptions @xcite , they still suffer from diversity and struggle with out-of-distribution compositional generalization @xcite . one way to alleviate these issues is to recombine different data points to produce novel training data, i.e., compositional data augmentation @xcite . however, existing compositional da mainly focus on editing short sentences locally with words/phrases/parsing trees @xcite , neglecting rich structural information between different sets of utterances in conversations @xcite , which prevent them from being applied to conversations to compose multiple utterances and generate novel, diverse and high-quality conversational data. we visualize one example with the topic structures @xcite @xcite highlighted in figure 1 . the conversation consists of several topics: "opening", "explanation", "plan", etc. and we consider every topic snippet as the basic unit. in the meantime , we extracted the "action" triples @xcite to represent each topic snippet. with these topic snippets and action representations, we obtain the units for compositional operations. for instance, the blue topic split and summary sentence about the meeting plan could be composed into another conversation by substitution to produce a new conversation and summary that contains a meeting plan. as it shows, by extracting the topic structures from the conversations, sub-components of conversations can be re-organized and re-composed to generate augmented conversation-summary pairs that might not be seen in the original corpus, resulting in more diverse training data. to this end, we propose compo, a compositional data augmentation framework operating at sub-structure level. we leverage the conversation structures (i.e., topic structure(chen and yang, 2020) and action triples @xcite ) to produce compositional units for generating diverse conversation-summary pairs. specifically, we first segment conversation into topic splits with topic modeling models, and then extract "actions triples" @xcite to represent each split as actions express specific socially situated identities and activities. with the extracted structures, we view the topic snippets as the basic units and perform selective retrieval based on action triples for compositional substitution to generate novel and diverse conversations. we also pair topic splits with summary sentences so that new summaries would be generated as well. an example of newly augmented conversation and summary could be found in figure 2(b ). to better leverage the newly generated conversation-summary pairs from compo, we further explore two noisetolerant methods including a self-training framework that uses the new conversations only, and another joint-training framework that leverages paired data. empirical studies verify compo's effectiveness via both quantitative and qualitative evaluations on samsum @xcite and di-alogsum @xcite compared to prior state-of-the-art data augmentation techniques. we also illustrate compo's transferability on a news summarization dataset cnn/dailymail. ## methodology to generate diverse conversation-summary pairs to deal with the data scarcity issue, this section presents a simple and effective compositional data augmentation method compo for supervised abstractive conversation summarization. the framework is illustrated in figure 2 . ## conclusion this paper introduced a simple and effective compositional data augmentation method for abstractive conversation summarization. we leverage the topical view of conversations and treat them as the units for compositional operation. extensive experiments on benchmark datasets demonstrate that compo significantly outperforms prior state-ofthe-art baselines in terms of both quantitative and qualitative evaluation, through generating compositional and diverse augmented data. our method has key implications for designing augmentation techniques for low-resource dialogue-related tasks.