Embedding Knowledge for Document Summarization: A Survey

• URL: http://arxiv.org/abs/2204.11190v1
• Date: Sun, 24 Apr 2022 04:36:07 GMT
• Title: Embedding Knowledge for Document Summarization: A Survey
• Authors: Yutong Qu, Wei Emma Zhang, Jian Yang, Lingfei Wu, Jia Wu and Xindong Wu
• Abstract summary: Previous works proved that knowledge-embedded document summarizers excel at generating superior digests. We propose novel to recapitulate knowledge and knowledge embeddings under the document summarization view.
• Score: 66.76415502727802
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge-aware methods have boosted a range of Natural Language Processing applications over the last decades. With the gathered momentum, knowledge recently has been pumped into enormous attention in document summarization research. Previous works proved that knowledge-embedded document summarizers excel at generating superior digests, especially in terms of informativeness, coherence, and fact consistency. This paper pursues to present the first systematic survey for the state-of-the-art methodologies that embed knowledge into document summarizers. Particularly, we propose novel taxonomies to recapitulate knowledge and knowledge embeddings under the document summarization view. We further explore how embeddings are generated in learning architectures of document summarization models, especially in deep learning models. At last, we discuss the challenges of this topic and future directions.

Related papers

• From References to Insights: Collaborative Knowledge Minigraph Agents for Automating Scholarly Literature Review [22.80918934436901]
  This paper proposes a novel framework, collaborative knowledge minigraph agents (CKMAs) to automate scholarly literature reviews. A novel prompt-based algorithm, the knowledge minigraph construction agent (KMCA), is designed to identify relationships between information pieces from academic literature. By leveraging the capabilities of large language models on constructed knowledge minigraphs, the multiple path summarization agent (MPSA) efficiently organizes information pieces and relationships from different viewpoints to generate literature review paragraphs.
  arXiv  Detail & Related papers  (2024-11-09T12:06:40Z)
• Knowledge-augmented Deep Learning and Its Applications: A Survey [60.221292040710885]
  knowledge-augmented deep learning (KADL) aims to identify domain knowledge and integrate it into deep models for data-efficient, generalizable, and interpretable deep learning. This survey subsumes existing works and offers a bird's-eye view of research in the general area of knowledge-augmented deep learning.
  arXiv  Detail & Related papers  (2022-11-30T03:44:15Z)
• KALM: Knowledge-Aware Integration of Local, Document, and Global Contexts for Long Document Understanding [27.4842322089676]
  KALM is a Knowledge-Aware Language Model that jointly leverages knowledge in local, document-level, and global contexts. It achieves state-of-the-art performance on six long document understanding tasks and datasets.
  arXiv  Detail & Related papers  (2022-10-08T20:51:02Z)
• Knowledge-Aware Bayesian Deep Topic Model [50.58975785318575]
  We propose a Bayesian generative model for incorporating prior domain knowledge into hierarchical topic modeling. Our proposed model efficiently integrates the prior knowledge and improves both hierarchical topic discovery and document representation.
  arXiv  Detail & Related papers  (2022-09-20T09:16:05Z)
• A Survey of Deep Learning Approaches for OCR and Document Understanding [68.65995739708525]
  We review different techniques for document understanding for documents written in English. We consolidate methodologies present in literature to act as a jumping-off point for researchers exploring this area.
  arXiv  Detail & Related papers  (2020-11-27T03:05:59Z)
• Multi-document Summarization via Deep Learning Techniques: A Survey [29.431160110691607]
  We propose a novel taxonomy to summarize the design strategies of neural networks. We highlight the differences between various objective functions that are rarely discussed in the existing literature.
  arXiv  Detail & Related papers  (2020-11-10T00:35:46Z)
• Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised Approach [89.56158561087209]
  We study summarizing on arbitrary aspects relevant to the document. Due to the lack of supervision data, we develop a new weak supervision construction method and an aspect modeling scheme. Experiments show our approach achieves performance boosts on summarizing both real and synthetic documents.
  arXiv  Detail & Related papers  (2020-10-14T03:20:46Z)
• Neural Topic Modeling with Continual Lifelong Learning [19.969393484927252]
  We propose a lifelong learning framework for neural topic modeling. It can process streams of document collections, accumulate topics and guide future topic modeling tasks. We demonstrate improved performance quantified by perplexity, topic coherence and information retrieval task.
  arXiv  Detail & Related papers  (2020-06-19T00:43:23Z)
• From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information [77.89755281215079]
  Text summarization is the research area aiming at creating a short and condensed version of the original document. In real-world applications, most of the data is not in a plain text format. This paper focuses on the survey of these new summarization tasks and approaches in the real-world application.
  arXiv  Detail & Related papers  (2020-05-10T14:59:36Z)

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.