Key Point Analysis via Contrastive Learning and Extractive Argument Summarization

• URL: http://arxiv.org/abs/2109.15086v1
• Date: Thu, 30 Sep 2021 12:54:26 GMT
• Title: Key Point Analysis via Contrastive Learning and Extractive Argument Summarization
• Authors: Milad Alshomary, Timon Gurke, Shahbaz Syed, Philipp Heinrich, Maximilian Splieth\"over, Philipp Cimiano, Martin Potthast, Henning Wachsmuth
• Abstract summary: This paper presents our proposed approach to the Key Point Analysis shared task, collocated with the 8th Workshop on Argument Mining. 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.
• Score: 26.104816072770305
• License: http://creativecommons.org/licenses/by/4.0/
• 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, collocated 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.

Related papers

• Enhancing Argument Summarization: Prioritizing Exhaustiveness in Key Point Generation and Introducing an Automatic Coverage Evaluation Metric [3.0754290232284562]
  Key Point Analysis (KPA) task formulates argument summarization as representing the summary of a large collection of arguments. A sub-task of KPA, called Key Point Generation (KPG), focuses on generating these key points given the arguments. This paper introduces a novel extractive approach for key point generation, that outperforms previous state-of-the-art methods for the task.
  arXiv  Detail & Related papers  (2024-04-17T23:00:29Z)
• Exploring Key Point Analysis with Pairwise Generation and Graph Partitioning [61.73411954056032]
  Key Point Analysis (KPA) continues to be a significant and unresolved issue within the field of argument mining. We propose a novel approach for KPA with pairwise generation and graph partitioning.
  arXiv  Detail & Related papers  (2024-04-17T13:44:29Z)
• Learning Feature Matching via Matchable Keypoint-Assisted Graph Neural Network [52.29330138835208]
  Accurately matching local features between a pair of images is a challenging computer vision task. Previous studies typically use attention based graph neural networks (GNNs) with fully-connected graphs over keypoints within/across images. We propose MaKeGNN, a sparse attention-based GNN architecture which bypasses non-repeatable keypoints and leverages matchable ones to guide message passing.
  arXiv  Detail & Related papers  (2023-07-04T02:50:44Z)
• Do You Hear The People Sing? Key Point Analysis via Iterative Clustering and Abstractive Summarisation [12.548947151123555]
  Argument summarisation is a promising but currently under-explored field. One of the main challenges in Key Point Analysis is finding high-quality key point candidates. evaluating key points is crucial in ensuring that the automatically generated summaries are useful.
  arXiv  Detail & Related papers  (2023-05-25T12:43:29Z)
• Comparing Methods for Extractive Summarization of Call Centre Dialogue [77.34726150561087]
  We experimentally compare several such methods by using them to produce summaries of calls, and evaluating these summaries objectively. We found that TopicSum and Lead-N outperform the other summarisation methods, whilst BERTSum received comparatively lower scores in both subjective and objective evaluations.
  arXiv  Detail & Related papers  (2022-09-06T13:16:02Z)
• Beyond Farthest Point Sampling in Point-Wise Analysis [52.218037492342546]
  We propose a novel data-driven sampler learning strategy for point-wise analysis tasks. We learn sampling and downstream applications jointly. Our experiments show that jointly learning of the sampler and task brings remarkable improvement over previous baseline methods.
  arXiv  Detail & Related papers  (2021-07-09T08:08:44Z)
• MatchVIE: Exploiting Match Relevancy between Entities for Visual Information Extraction [48.55908127994688]
  We propose a novel key-value matching model based on a graph neural network for VIE (MatchVIE) Through key-value matching based on relevancy evaluation, the proposed MatchVIE can bypass the recognitions to various semantics. We introduce a simple but effective operation, Num2Vec, to tackle the instability of encoded values.
  arXiv  Detail & Related papers  (2021-06-24T12:06:29Z)
• Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding [71.2260967797055]
  We propose a weakly-supervised approach for aspect-based sentiment analysis. We learn sentiment, aspect> joint topic embeddings in the word embedding space. We then use neural models to generalize the word-level discriminative information.
  arXiv  Detail & Related papers  (2020-10-13T21:33:24Z)
• Quantitative Argument Summarization and Beyond: Cross-Domain Key Point Analysis [17.875273745811775]
  We develop a method for automatic extraction of key points, which enables fully automatic analysis. We demonstrate that the applicability of key point analysis goes well beyond argumentation data. An additional contribution is an in-depth evaluation of argument-to-key point matching models.
  arXiv  Detail & Related papers  (2020-10-11T23:01:51Z)
• From Arguments to Key Points: Towards Automatic Argument Summarization [17.875273745811775]
  We show 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.
  arXiv  Detail & Related papers  (2020-05-04T16:24:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.