GENDEX : Generative Data Augmentation Strategy Leveraging External Data for Abstractive Dialogue Summarization (2024)

Abstract: With the proliferation of digital communication, dialogue summarization has become increasingly important. However, it still faces a shortage of data. To address this issue, we developed **Gen**erative **D**ata Augmentation Strategy Leveraging **Ex**ternal Data for Abstractive Dialogue Summarization (**GENDEX**), which is based on the hypothetical foundation that texts containing people and their interpersonal interactions can potentially serve as summaries of corresponding dialogues. We filter short texts containing people and resolve coreferences for better contextual analysis. We then identify the semantic roles of words within the texts and filter them based on the patterns observed in the dialogue summarization datasets. Using these texts, we generate synthetic dialogues through a controlled generation method. To better leverage the augmented data, we utilize noise-tolerant training to fine-tune the summarization model. The experimental results demonstrate the effectiveness of our proposed method, showing its robust performance, generalizability, and scalability. Moreover, performance improvements by *GENDEX* were observed regardless of complexity of dialogues. The code is available at https://github.com/DMCB-GIST/GENDEX.

## introduction text summarization is to generate short texts containing essential information @xcite . in an era overflowing with web information, it can serve as a convenient tool. to date, many summarization studies have focused on document data. recently, the need for dialogue summarization has emerged, following the growing usage of mobile communications and social network services. dialogue summarization is not just about reducing content but also enhancing comprehension and the utility of conversations by making information more * hyunju lee is the corresponding author. however, there is a significant lack of dialogue summarization data, making it a more challenging task than document summarization (see to appendix a for comparative results). there are some reasons for this. while document summarization datasets can be collected in an automated manner by utilizing titles, headlines, and abstracts as summaries, dialogue data require manual annotations @xcite . moreover, there are privacy issues of real dialogues, which must be addressed to publicize them @xcite . further, dialogue data are structurally complex. dialogues inherently consist of turns. this turn-based structure is complex compared to the straightforward structure of plaintext-based documents. these properties complicate the collection, organization, and publicization of dialogue data. to address data shortage, various approaches have been explored. one way is transfer learning utilizing abundant document summarization data to benefit from adaptation @xcite . another way is a simple perturbationbased method at the token level, such as synonym replacement and random word deletion @xcite @xcite . @xcite introduced a simple augmentation method that slightly perturbs the original data at the utterance level, considering conversational characteristics. @xcite used the original training data for reverse-training and synthesizing new dialogues. although these approaches have improved the dialogue summarization performance, several limitations still remain. transfer learning methods may figure 1 : example of the dialogue summarization data from samsum @xcite . figure 1 shows an example of dialogue summarization data. dialogue summaries generally include people who appear in the dialogues and their interpersonal interactions. we analyzed three popular dialogue summarization datasets and found out that 97 -99% of summaries contain people (see appendix b for more details). this finding emphasizes the critical role that person names play, suggesting their presence as a key element for capturing the context of dialogues. motivated by these, we hypothesize that texts including people and their interpersonal interactions can potentially serve as summaries of the corresponding dialogues. building on this insight, we propose generative data augmentation strategy leveraging external data for abstractive dialogue summarization (gendex). to address the shortage of dialogue data, we utilize abundant external outof-domain (ood) data. we apply named entity recognition (ner) to select texts containing person names, resolve coreferences for context, and determine semantic roles to filter texts aligning with dialogue summary patterns. these steps enable us to generate dialogues from curated texts. we then generate synthetic data using controlled generation. lastly, we use two-stage noise-tolerant training to better utilize synthetic data. experimental results demonstrate that gendex is an effective technique in various aspects. it not only performs well in both quantitative and qualitative evaluations but also exhibits generalizability and scalability. in addition, it improves performance regardless of complexity of dialogues. ## related works as dialogue summarization has recently received much attention, several datasets have been proposed in various domains, such as meetings @xcite , chats @xcite , and customer services @xcite . several methods have been proposed for effective dialogue summarization. most initial works directly used document summarization models for dialogue summarization @xcite . subsequently, the structural and contextual information of the dialogue data was explored. chen and yang (2020) extracted conversational structures from different views and then incorporated them for better representation. @xcite enhanced the understanding of dialogues by introducing discourse relations using a relational graph encoder. chen and yang (2021b) incorporated discourse relations and action triples to better represent interactions. @xcite explored the transfer learning between document and dialogue data. @xcite proposed a pre-training framework for long dialogue summarization task. however, many models still require large amounts of data to achieve cutting-edge performance @xcite . several methods have been proposed to improve dialogue summarization performance in such low-resource environments. @xcite utilized abundant document datasets for adaptation. additional techniques, such as data modification and augmentation have also been proposed. @xcite proposed a data augmentation method consisting of swapping, deletion, insertion, and substitution at utterance level. other augmentation methods by replacing text sections in dialogue and summary using pre-trained language model @xcite were also introduced. @xcite proposed a generative augmentation technique using a portion of the training data. sally will buy a new smartphone tomorrow. ted will help her. 𝐷 = {𝑡 0 , 𝑡 1 , … , 𝑡 𝑛 }: texts from ood datasets books were scattered across the table. ## method in this section, we introduce gendex, an effective generative data augmentation strategy for abstractive dialogue summarization. our approach begins with source data filtering process to ensure the quality of the seed texts for generation (section 3.1). we then generate synthetic data (section 3.2). finally, we train the dialogue summarization model to better utilize the augmented data (section 3.3). the overall pipeline of gendex is illustrated in figure 2 . ## conclusion in this paper, we proposed a generative data augmentation strategy leveraging external data for abstractive dialogue summarization, called gendex. we filter ood texts by focusing on the presence of person and interpersonal interactions in the texts. we generate synthetic dialogues from these filtered texts and augment dialogue summarization data. this approach enhances data diversity in terms of semantics and introduces external knowledge by leveraging ood data and reconstructing it in a dialogue format. the experimental results show that gendex can improve the performance of dialogue summarization in both quantitative and qualitative aspects, outperforming previous stateof-the-art methods. they also demonstrate the effectiveness of gendex, highlighting its robust performance, generalizability, and scalability. furthermore, gendex improved the performance of dialogue summarization regardless of the complexity of dialogue data.