Key Point Analysis via Contrastive Learning and Extractive Argument Summarization (2021)

Abstract: Key point analysis is the task of extracting a set of concise and high-level statements from a given collection of arguments, representing the gist of these arguments. This paper presents our proposed approach to the Key Point Analysis Shared Task, colocated with the 8th Workshop on Argument Mining. The approach integrates two complementary components. One component employs contrastive learning via a siamese neural network for matching arguments to key points; the other is a graph-based extractive summarization model for generating key points. In both automatic and manual evaluation, our approach was ranked best among all submissions to the shared task.

## introduction informed decision-making on a controversial issue usually requires considering several pro and con arguments. to answer the question "is organic food healthier?", for example, people may query a search engine that retrieves arguments from diverse sources such as news editorials, debate portals, and social media discussions, which can then be compared and weighed. however, given the constant stream of digital information, this process may be time-intensive and overwhelming. search engines and similar support systems may therefore benefit from employing argument summarization, that is, the generated summaries may aid the decisionmaking by helping users quickly choose relevant arguments with a specific stance towards the topic. argument summarization has been tackled both for single documents @xcite and multiple documents @xcite . a specific multi-document scenario introduced by @xcite is key point analysis where the goal is to map a collection of arguments to a set of salient key points (say, high-level arguments) to provide a quantitative summary of these arguments. the key point analysis (kpa) shared task by @xcite foot_0 invited systems for two complementary subtasks: matching arguments to key points and generating key points from a given set of arguments (section 3). as part of this shared task, we present an approach with two complementary components, one for each subtask. for key point matching, we propose a model that learns a semantic embedding space where instances that match are closer to each other while non-matching instances are further away from each other. we learn to embed instances by utilizing a contrastive loss function in a siamese neural network @xcite . for the key point generation, we present a graph-based extractive summarization approach similar to the work of @xcite . it utilizes a pagerank variant to rank sentences in the input arguments by quality and predicts the top-ranked sentences to be key points. in an additional experiment, we also investigated an approach that performs aspect identification on arguments, followed by aspect clustering to ensure diversity. finally, arguments with the best coverage of these diverse aspects are extracted as key points. our approaches yielded the top performance among all submissions to the shared task in both quantitative and qualitative evaluation conducted by the organizers of the shared task (section 5). 2 . ## related work in summarization, arguments are relatively understudied compared to other document types such as news articles or scientific literature, but a few approaches have come up in the last years. in an extractive manner, argument mining has been employed to identify the main claim as the summary of an argument @xcite . @xcite used a sequence-to-sequence model for the abstractive summarization of arguments from online debate portals. a complementary task of generating conclusions as informative argument summaries was introduced by @xcite . similar to @xcite who inferred a conclusion's target with a triplet neural network, we rely on contrastive learning here, using a siamese network though. also, we build upon ideas of @xcite who proposed a graph-based model using pagerank @xcite that extracts the argument's conclusion and the main supporting reason as an extractive summary. all these works represent the single-document summarization paradigm where only one argument is summarized at a time, whereas the given shared task is a multi-document summarization setting. the first approaches to multi-document argument summarization aimed to identify the main points of online discussions. among these, @xcite grouped verb frames into pattern clusters that serve as input to a structured summarization pipeline, whereas @xcite proposed a more condensed approach by directly extracting argumentative sentences, summarized by similarity clustering. @xcite continued this line of research by introducing the notion of key points and contributing the argskp corpus, a collection of arguments mapped to manually-created key points. these key points are concise and selfcontained sentences that capture the gist of the arguments. later, @xcite proposed a quantitative argument summarization framework that automatically extracts key points from a set of arguments. building upon this research, our approach aims to increase the quality of such generated key points, including a strong relation identifier between arguments and key points. ## task description in the context of computational argumentation, @xcite introduced the notion of a key point as a high-level argument that resembles a natural language summary of a collection of more descriptive arguments. specifically, the authors defined a good key point as being "general enough to match a significant portion of the arguments, yet informative enough to make a useful summary." in this context, the kpa shared task consists of two subtasks as described below: ## approach our approach consists of two components, each corresponding to one subtask of the kpa shared task. the first subtask of matching arguments to key points is modeled as a contrastive learning task using a siamese neural network. the second subtask requires generating key points for a collection of arguments and then matching them to the arguments. we investigated two models for this subtask: one is a graph-based extractive summarization model utilizing pagerank @xcite to extract sentences representing the key points; the other identifies aspects from the arguments and selects the most representative sentences that maximize the coverage of these aspects as the key points. ## experiments and evaluation in the following, we present implementation details of our two components, and we report on their quantitative and qualitative results. ## conclusion this paper has presented a framework to tackle the key point analysis of arguments. for matching arguments to key points, we achieved the best performance in the kpa shared task via contrastive learning. for key point generation, we developed a graph-based extractive summarization model that output informative key points of high quality for a collection of arguments. we see abstractive key point generation as part of our future work.