Aspect-Based Argument Mining (2020)

Abstract: Computational Argumentation in general and Argument Mining in particular are important research fields. In previous works, many of the challenges to automatically extract and to some degree reason over natural language arguments were addressed. The tools to extract argument units are increasingly available and further open problems can be addressed. In this work, we are presenting the task of Aspect-Based Argument Mining (ABAM), with the essential subtasks of Aspect Term Extraction (ATE) and Nested Segmentation (NS). At the first instance, we create and release an annotated corpus with aspect information on the token-level. We consider aspects as the main point(s) argument units are addressing. This information is important for further downstream tasks such as argument ranking, argument summarization and generation, as well as the search for counter-arguments on the aspect-level. We present several experiments using state-of-the-art supervised architectures and demonstrate their performance for both of the subtasks. The annotated benchmark is available at https://github.com/trtm/ABAM .

## introduction the field of computational argumentation @xcite gained a lot of interest in the last couple of years. this is noticeable from both the number of the submitted publications related to this field and also from the high volume of emerging datasets @xcite @xcite @xcite , specific task formulations @xcite and models @xcite . similar to aspect-based sentiment analysis @xcite , we also see the possibility of breaking down arguments into smaller attributes or meaningful components in the argument mining domain. we consider these components as aspects of the arguments. previous works already utilized aspectinformation for several subtasks within the argument mining domain @xcite @xcite . however, these works vary significantly in the definition of aspects and do not focus on the aspect-based argument mining explicitly, e.g., employ aspects as a source of side or additional information. for instance, @xcite are mainly focusing on the summarization of opinions, visualizing pro and contra arguments for a given topic. thereby, the authors are extracting aspects, calling them points at issue, and ranking the arguments according to them. however, their approach relies on rule-based extraction solely. in @xcite , the authors are proposing summarization methods to recognize specific arguments and counter-arguments in social media texts, to further group them across discussions into facets (i.e., aspects) on which that issue is argued. still, this work is limited to a couple of topics and samples. finally, @xcite also mention aspects as part of four functional components, where the authors interchangeably label aspects and concepts for the specific words. however, to the best of our knowledge, the authors did not publish their labeled data, making a comparative evaluation of aspect extraction methods impossible. we, in contrast, specifically address the aspect term supporters say it is an unnecessary regulation designed to force clinics to shut down, while opponents say the prohibition protects women's health. ## problem statement we define the abam task as following: given a list of several topic related texts (documents or paragraphs), we segment the texts into n sentences sentence @xmath0, t 2 , t 3 , . . . , t n ] tokens outside of argumentative spans are assigned the n on stance label. furthermore, regularly there is at least one aspect in every selected span with ## related work regarding the abovementioned problem definition ( §2), we selected three research areas as thematically closed to our task. ## corpus creation the creation of the abam benchmark is based on the argument units from the aurc corpus @xcite and is divided into two main parts. the first part addresses two studies for the annotation task formulation, whereas the second part describes the final corpus creation. we outsourced the data annotation to independent (crowd-)annotators and based on their results we created the gold labels. ## experimental setup this section presents our experimental setup regarding the two tasks, the employed models and the data set splits. ## results this section presents the results for our tasks as described in §5.1. ## error analysis recalling our definition of aspects: they are defined as the main point(s) argument units are addressing. furthermore, considering our annotation guidelines in §a, the most important point is usually not equal to the given main topic. an overview of the main errors found during the evaluation of the development sets for the best performing models in the inner-and cross-topic set-ups, is given below. aspect term extraction during the evaluation of ate results, we observed a number of errors, which we grouped into the following categories: ## conclusion abam is a challenging task that, to the best of our knowledge, was not directly addressed before. we made two important contributions: first, we created and released a publicly available benchmark for aspect-based argument mining. second, we showcased several baselines for the two subtasks, namely the aspect term extraction and the nested segmentation, and performed an elaborative error analysis. we believe that these findings as well as the benchmark are of high potential for further downstream tasks, such as argument ranking, argument summarization and the search for counter-arguments on the aspect-level. for the future work, we foresee the investigation of unsupervised approaches for the aspect term extraction task, since they showed promising results within the aspect-based sentiment analysis domain.