Keyphrase Extraction with Dynamic Graph Convolutional Networks and Diversified Inference

• URL: http://arxiv.org/abs/2010.12828v1
• Date: Sat, 24 Oct 2020 08:11:23 GMT
• Title: Keyphrase Extraction with Dynamic Graph Convolutional Networks and Diversified Inference
• Authors: Haoyu Zhang, Dingkun Long, Guangwei Xu, Pengjun Xie, Fei Huang, Ji Wang
• Abstract summary: 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.
• Score: 50.768682650658384
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Keyphrase extraction (KE) aims to summarize a set of phrases that accurately express a concept or a topic covered in a given document. Recently, Sequence-to-Sequence (Seq2Seq) based generative framework is widely used in KE task, and it has obtained competitive performance on various benchmarks. The main challenges of Seq2Seq methods lie in acquiring informative latent document representation and better modeling the compositionality of the target keyphrases set, which will directly affect the quality of generated keyphrases. In this paper, we propose to adopt the Dynamic Graph Convolutional Networks (DGCN) to solve the above two problems simultaneously. Concretely, we explore to integrate dependency trees with GCN for latent representation learning. Moreover, the graph structure in our model is dynamically modified during the learning process according to the generated keyphrases. To this end, our approach is able to explicitly learn the relations within the keyphrases collection and guarantee the information interchange between encoder and decoder in both directions. Extensive experiments on various KE benchmark datasets demonstrate the effectiveness of our approach.

Related papers

• Fine-tuning CLIP Text Encoders with Two-step Paraphrasing [83.3736789315201]
  We introduce a straightforward fine-tuning approach to enhance the representations of CLIP models for paraphrases. Our model, which we call ParaCLIP, exhibits significant improvements over baseline CLIP models across various tasks.
  arXiv  Detail & Related papers  (2024-02-23T06:11:50Z)
• SimCKP: Simple Contrastive Learning of Keyphrase Representations [36.88517357720033]
  We propose SimCKP, a simple contrastive learning framework that consists of two stages: 1) An extractor-generator that extracts keyphrases by learning context-aware phrase-level representations in a contrastive manner while also generating keyphrases that do not appear in the document; and 2) A reranker that adapts scores for each generated phrase by likewise aligning their representations with the corresponding document.
  arXiv  Detail & Related papers  (2023-10-12T11:11:54Z)
• Conversational Semantic Parsing using Dynamic Context Graphs [68.72121830563906]
  We consider the task of conversational semantic parsing over general purpose knowledge graphs (KGs) with millions of entities, and thousands of relation-types. We focus on models which are capable of interactively mapping user utterances into executable logical forms.
  arXiv  Detail & Related papers  (2023-05-04T16:04:41Z)
• Pre-trained Language Models for Keyphrase Generation: A Thorough Empirical Study [76.52997424694767]
  We present an in-depth empirical study of keyphrase extraction and keyphrase generation using pre-trained language models. We show that PLMs have competitive high-resource performance and state-of-the-art low-resource performance. Further results show that in-domain BERT-like PLMs can be used to build strong and data-efficient keyphrase generation models.
  arXiv  Detail & Related papers  (2022-12-20T13:20:21Z)
• Representation Learning for Resource-Constrained Keyphrase Generation [78.02577815973764]
  We introduce salient span recovery and salient span prediction as guided denoising language modeling objectives. We show the effectiveness of the proposed approach for low-resource and zero-shot keyphrase generation.
  arXiv  Detail & Related papers  (2022-03-15T17:48:04Z)
• 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)
• WSRNet: Joint Spotting and Recognition of Handwritten Words [38.212002652391]
  The proposed network is comprised of a non-recurrent CTC branch and a Seq2Seq branch that is further augmented with an Autoencoding module. We show how to further process these representations with binarization and a retraining scheme to provide compact and highly efficient descriptors.
  arXiv  Detail & Related papers  (2020-08-17T06:22:05Z)
• Keyphrase Prediction With Pre-trained Language Model [16.06425973336514]
  We propose to divide the keyphrase prediction into two subtasks, i.e., present keyphrase extraction (PKE) and absent keyphrase generation (AKG) For PKE, we tackle this task as a sequence labeling problem with the pre-trained language model BERT. For AKG, we introduce a Transformer-based architecture, which fully integrates the present keyphrase knowledge learned from PKE by the fine-tuned BERT.
  arXiv  Detail & Related papers  (2020-04-22T09:35:02Z)
• Exclusive Hierarchical Decoding for Deep Keyphrase Generation [63.357895318562214]
  Keyphrase generation (KG) aims to summarize the main ideas of a document into a set of keyphrases. Previous work in this setting employs a sequential decoding process to generate keyphrases. We propose an exclusive hierarchical decoding framework that includes a hierarchical decoding process and either a soft or a hard exclusion mechanism.
  arXiv  Detail & Related papers  (2020-04-18T02:58:00Z)
• Keyphrase Extraction with Span-based Feature Representations [13.790461555410747]
  Keyphrases are capable of providing semantic metadata characterizing documents. Three approaches to address keyphrase extraction: (i) traditional two-step ranking method, (ii) sequence labeling and (iii) generation using neural networks. In this paper, we propose a novelty Span Keyphrase Extraction model that extracts span-based feature representation of keyphrase directly from all the content tokens.
  arXiv  Detail & Related papers  (2020-02-13T09:48:31Z)

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.