From Arguments to Key Points: T owards Automatic Argument Summarization (2020)

Abstract: Generating a concise summary from a large collection of arguments on a given topic is an intriguing yet understudied problem. We propose to represent such summaries as a small set of talking points, termed key points , each scored according to its salience. We show, by analyzing a large dataset of crowd-contributed arguments, that a small number of key points per topic is typically sufficient for covering the vast majority of the arguments. Furthermore, we found that a domain expert can often predict these key points in advance. We study the task of argument-to-key point mapping, and introduce a novel large-scale dataset for this task. We report empirical results for an extensive set of experiments with this dataset, showing promising performance.

## introduction governments, businesses and individuals, all need to make decisions on a daily basis: "should cannabis be legalized?","should we develop this product?", "should i become a vegetarian?". when making an important decision, the process typically comprises several steps: first, we gather as much information as we can about the pros and cons of the proposal under consideration. we may then summarize the collected information as a short list of the main arguments for each side. lastly, we aim to weigh the pro and con arguments against each other to make the final decision. where can we find relevant arguments for a given topic? in recent years, significant progress was made in the field of argument mining, automatic identification and extraction of argumentative structures in text @xcite . specifically, several works focused on topic-related argument mining from the web or other massive corpora @xcite @xcite @xcite . policy makers in governments or businesses may also conduct surveys to collect from large audiences arguments supporting or contesting some proposal. each of the above methods may result in hundreds or thousands of arguments per topic, making it impossible for the decision maker to read and digest such large amounts of information. several works aimed to alleviate this problem by clustering together related arguments, based on different notions of relatedness, such as similarity @xcite , frames @xcite , and argument facets @xcite . these works, however, did not attempt to create a concise textual summary from the resulting clusters. in this work we propose to summarize the arguments supporting each side of the debate by mapping them to a short list of talking points, termed key points. the salience of each key point can be represented by the number of its matching arguments. an example for such summary is shown in the proposed method raises a fundamental question: can a small number of key points effectively summarize massive amount of arguments collected from a large population? in this work we give a positive answer to this question, based on extensive analysis over 28 controversial topics and 7,000 crowd-contributed pro and con arguments for these topics. furthermore, we found that, given a controversial topic, a domain expert can compose a short, comprehensive list of key points even without looking at the arguments themselves. motivated by the above findings, we assume in this work that the key points for each topic are given, and focus on the task of automatically map-homeschooling should be banned #args pro mainstream schools are essential to develop social skills. 61 parents are not qualified as teachers. 20 homeschools cannot be regulated/standardized. 15 mainstream schools are of higher educational quality. 9 con parents should be permitted to choose the education of their children. ## conclusion this work addressed the practical problem of summarizing a large collection of arguments on a given topic. we proposed to represent such summaries as a set of key points scored according to their relative salience. such summary aims to provide both textual and quantitative views of the argument data in a concise form. we demonstrated the feasibility and effectiveness of the proposed approach through extensive data annotation and analysis. we showed that a domain expert can quickly come up with a short list of pro and con key points per topic, that would capture the gist of crowd-contributed arguments, even without being exposed to the arguments themselves. we studied the problem of automatically matching arguments to key points, and developed the first large-scale dataset for this task, which we make publicly available. our experimental results demonstrate that the problem is far from trivial, and cannot be effectively solved using unsupervised methods based on word or sentence-level embedding. however, by using state of the art supervised learning methods for match scoring, together with an appropriate key point selection policy for match classification, we were able to achieve promising results on this task. the natural next step for this work is the challenging task of automatic key point generation. in addition, we plan to apply the methods presented in this work also to automatically-mined arguments. finally, detecting the more implicit relations between the argument and the key point, as seen in our error analysis, is another intriguing direction for future work.