Erick A. Mendez Guzman ^1* Viktor Schlegel ² Riza Batista-Navarro ¹

• ¹ The University of Manchester, Manchester, United Kingdom
• ² ASUS Intelligent Cloud Services, Singapore, Singapore

Deep learning models have achieved state-of-the-art performance for text classification in the last two decades. However, this has come at the expense of models becoming less understandable, limiting their application scope in high-stakes domains. The increased interest in explainability has resulted in many proposed forms of explanation. Nevertheless, recent studies have shown that rationales, or language explanations, are more intuitive and humanunderstandable, especially for non-technical stakeholders. This survey provides an overview of the progress the community has achieved thus far in rationalisation approaches for text classification. We first describe and compare techniques for producing extractive and abstractive rationales. Next, we present various rationale-annotated data sets that facilitate the training and evaluation of rationalisation models. Then, we detail proxy-based and human-grounded metrics to evaluate machine-generated rationales. Finally, we outline current challenges and encourage directions for future work.