Exploring Key Point Analysis with Pairwise Generation and Graph Partitioning (2024)

Abstract: Key Point Analysis (KPA), the summarization of multiple arguments into a concise collection of key points, continues to be a significant and unresolved issue within the field of argument mining. Existing models adapt a two-stage pipeline of clustering arguments or generating key points for argument clusters. This approach rely on semantic similarity instead of measuring the existence of shared key points among arguments. Additionally, it only models the intra-cluster relationship among arguments, disregarding the inter-cluster relationship between arguments that do not share key points. To address these limitations, we propose a novel approach for KPA with pairwise generation and graph partitioning. Our objective is to train a generative model that can simultaneously provide a score indicating the presence of shared key point between a pair of arguments and generate the shared key point. Subsequently, to map generated redundant key points to a concise set of key points, we proceed to construct an arguments graph by considering the arguments as vertices, the generated key points as edges, and the scores as edge weights. We then propose a graph partitioning algorithm to partition all arguments sharing the same key points to the same subgraph. Notably, our experimental findings demonstrate that our proposed model surpasses previous models when evaluated on both the ArgKP and QAM datasets.

## introduction analyzing a large number of arguments and making decisions is a long standing issue present in real life scenarios such as product review analysis @xcite , opinion surveys @xcite , etc. however, manually reading through a vast amount of arguments is not a feasible solution. in this context, topic 𝒌𝒑 𝟏 𝒌𝒑 𝟐 school uniforms make life so much easier. there's less for parents to buy, a lot can't afford name brand clothing. less bullying due to clothing. it's easier to get ready in the morning. having a school uniform can reduce bullying as students who have no style or cannot afford the latest trends do not stand out. key point analysis @xcite has been proposed. kpa involves distilling a multitude of arguments into a concise set of key points, followed by a quantitative evaluation, typically by calculating the frequency of each key point related to arguments. an example is shown in figure 1 . existing methods and limitations. the generation of key points from arguments represents the most pivotal and challenging aspect of the kpa task. initially, a straightforward approach @xcite involves selecting a few short arguments as key points and then determining whether other arguments match these key points. subsequently, advanced model @xcite divides the kpa task into two stages. firstly, a semantic feature based clustering model is utilized to cluster arguments into multiple clusters. then, a generation model is employed to generate correspond-ing key points for arguments in each cluster by concatenating the arguments. however, these existing methods possess certain limitations. @xcite posits that the mere presence of appropriate key points within arguments is insufficient. @xcite s approach is based on clustering arguments using their semantic features and then positing that arguments within a cluster share the same key point. however, this approach does not fully align with the objectives of the kpa task. it is possible for arguments that are not semantically similar to still share a key point, as an argument can correspond to more than one key point. moreover, @xcite s approach only models the relationships between arguments that share a key point (i.e., intra-cluster arguments) and neglects the relationships between arguments that do not share a key point (i.e., inter-cluster arguments). our approach. in order to address the deficiencies mentioned above, we present an approach which trains a generative model to simultaneously utilize the relationships between intra-cluster and inter-cluster arguments. this model generates a key point for a pair of inter-cluster arguments and provided a score indicating whether there exists shared key points between the pair of arguments. during the prediction phase, we utilize the score provided by the generative model, which indicates whether a pair of arguments is inter-cluster arguments, rather than the semantic similarity of the arguments, to partition the arguments into multiple clusters. this score is provided by a fine-tuned generative model, which is aligned with the data distribution within the domain, rather than employing a clustering model that is unrelated to the domain. we then construct an argument graph and formulate the relationships among arguments as a graph-based problem. specifically, we represent arguments as vertices in the graph, the shared key point between two arguments as edges, and the scores as edge weights, resulting in a weighted argument graph. we introduce an iterative graph partitioning algorithm, which generates several subgraphs, each representing a collection of arguments that share the same or similar key point. from these subgraphs, we select a representative key point for further analysis and utilization. our algorithm evaluates the connections between arguments by the scores provided by the generative model, as semantic similarity may not fully align with the objectives of the kpa task. outline. we elaborate our approach in section 2, present experiments in section 3, discuss related work in section 4, and conclude in section 5. code. our code and data are available on github: https://github.com/alibaba-nlp/keypoint-analysis . ## approach on the kpa task, the input consists of a set of arguments @xmath0, stance 1 ), (arg 2 , stance 2 ), • • • } under a given topic t , where arg i is the argument text and stance i is the stance for arg i . the stance ("pro" or "con") indicate the stance of argument towards the topic. the ideal outcome is a concise set of key points @xmath1, kp 2 , • • • }, where each key point in k corresponds to one or more arguments in a. that is, our objective is to learn a surjective function f : a, @xmath2. afterwards, the importance of each key point can be expressed by calculating the percentage of arguments corresponding to each key point. figure 2 provides an overview of our proposed method, which comprises two principal components. the initial component is dedicated to the generation of key points, as depicted at the bottom of figure 2 . during this phase, we process the given topic t and pairs of arguments a × a to ascertain if each pair shares a key point and, if so, generate the corresponding key point. the subsequent component entails graph partitioning, illustrated at the top of figure 2 . in this stage, we construct a graph of arguments, key points, and their associated scores. we then apply our specially developed graph partitioning algorithm to divide the graph into multiple subgraphs, allowing for the possibility of the same argument being present in more than one subgraph, as a single argument may relate to multiple key points. finally, we select a representative key point from each subgraph to form the final concise set of key points k. notably, unlike previous pipeline approaches, we transmit the scores generated by the first stage to the second stage, avoiding issues such as feature propagation interruption and error accumulation that may arise in multi-stage pipelines. ## conclusion this paper presents a method that merges pairwise generation with graph partitioning to efficiently produce key points within arguments. our integrated approach models both the relationships between arguments sharing key points and those that do not, aiding in key point generation. we employ an iterative algorithm to partition the argument graph and extract representative key points from subgraphs, yielding a concise set of key points. in the future, we will continue to explore better ways to utilize generative models to model the relationships among arguments. for instance, we will investigate improved methods of outputting scores indicating whether arguments share key points and develop graph partition algorithms that better align with generative models. our approach may provide inspiration for tasks including clustering tasks based on generative models @xcite , set generation tasks @xcite , and so forth.