From Key Points to Key Point Hierarchy: Structured and Expressive Opinion Summarization

• URL: http://arxiv.org/abs/2306.03853v1
• Date: Tue, 6 Jun 2023 16:45:44 GMT
• Title: From Key Points to Key Point Hierarchy: Structured and Expressive Opinion Summarization
• Authors: Arie Cattan, Lilach Eden, Yoav Kantor and Roy Bar-Haim
• Abstract summary: Key Point Analysis (KPA) has been recently proposed for deriving fine-grained insights from collections of textual comments. We introduce the task of organizing a given set of key points into a hierarchy, according to their specificity. We develop ThinkP, a high quality benchmark dataset of key point hierarchies for business and product reviews.
• Score: 9.567280503274226
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Key Point Analysis (KPA) has been recently proposed for deriving fine-grained insights from collections of textual comments. KPA extracts the main points in the data as a list of concise sentences or phrases, termed key points, and quantifies their prevalence. While key points are more expressive than word clouds and key phrases, making sense of a long, flat list of key points, which often express related ideas in varying levels of granularity, may still be challenging. To address this limitation of KPA, we introduce the task of organizing a given set of key points into a hierarchy, according to their specificity. Such hierarchies may be viewed as a novel type of Textual Entailment Graph. We develop ThinkP, a high quality benchmark dataset of key point hierarchies for business and product reviews, obtained by consolidating multiple annotations. We compare different methods for predicting pairwise relations between key points, and for inferring a hierarchy from these pairwise predictions. In particular, for the task of computing pairwise key point relations, we achieve significant gains over existing strong baselines by applying directional distributional similarity methods to a novel distributional representation of key points, and further boost performance via weak supervision.

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)
• Meta-Point Learning and Refining for Category-Agnostic Pose Estimation [46.98479393474727]
  Category-agnostic pose estimation (CAPE) aims to predict keypoints for arbitrary classes given a few support images annotated with keypoints. We propose a novel framework for CAPE based on such potential keypoints (named as meta-points)
  arXiv  Detail & Related papers  (2024-03-20T14:54:33Z)
• KPEval: Towards Fine-Grained Semantic-Based Keyphrase Evaluation [69.57018875757622]
  We propose KPEval, a comprehensive evaluation framework consisting of four critical aspects: reference agreement, faithfulness, diversity, and utility. Using KPEval, we re-evaluate 23 keyphrase systems and discover that established model comparison results have blind-spots.
  arXiv  Detail & Related papers  (2023-03-27T17:45:38Z)
• Object Localization under Single Coarse Point Supervision [107.46800858130658]
  We propose a POL method using coarse point annotations, relaxing the supervision signals from accurate key points to freely spotted points. CPR constructs point bags, selects semantic-correlated points, and produces semantic center points through multiple instance learning (MIL) In this way, CPR defines a weakly supervised evolution procedure, which ensures training high-performance object localizer under coarse point supervision.
  arXiv  Detail & Related papers  (2022-03-17T14:14:11Z)
• UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction [20.26899340581431]
  Keyphrase Prediction task aims at predicting several keyphrases that can summarize the main idea of the given document. Mainstream KP methods can be categorized into purely generative approaches and integrated models with extraction and generation. We propose UniKeyphrase, a novel end-to-end learning framework that jointly learns to extract and generate keyphrases.
  arXiv  Detail & Related papers  (2021-06-09T07:09:51Z)
• Conditional Link Prediction of Category-Implicit Keypoint Detection [26.400925420154245]
  We propose an end-to-end category-implicit Keypoint and Link Prediction Network (KLPNet) In our KLPNet, a novel Conditional Link Prediction Graph is proposed for link prediction among keypoints that are contingent on a predefined category. Experiments conducted on three publicly available benchmarks demonstrate that our KLPNet consistently outperforms all other state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-11-29T23:00:37Z)
• Keyphrase Extraction with Dynamic Graph Convolutional Networks and Diversified Inference [50.768682650658384]
  Keyphrase extraction (KE) aims to summarize a set of phrases that accurately express a concept or a topic covered in a given document. Recent Sequence-to-Sequence (Seq2Seq) based generative framework is widely used in KE task, and it has obtained competitive performance on various benchmarks. In this paper, we propose to adopt the Dynamic Graph Convolutional Networks (DGCN) to solve the above two problems simultaneously.
  arXiv  Detail & Related papers  (2020-10-24T08:11:23Z)
• 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)

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.