ﻻ يوجد ملخص باللغة العربية
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.
The encoder-decoder framework achieves state-of-the-art results in keyphrase generation (KG) tasks by predicting both present keyphrases that appear in the source document and absent keyphrases that do not. However, relying solely on the source docum
These years much effort has been devoted to improving the accuracy or relevance of the recommendation system. Diversity, a crucial factor which measures the dissimilarity among the recommended items, received rather little scrutiny. Directly related
Text classification is an important and classical problem in natural language processing. There have been a number of studies that applied convolutional neural networks (convolution on regular grid, e.g., sequence) to classification. However, only a
Keyphrases are capable of providing semantic metadata characterizing documents and producing an overview of the content of a document. Since keyphrase extraction is able to facilitate the management, categorization, and retrieval of information, it h
AMR-to-text generation is used to transduce Abstract Meaning Representation structures (AMR) into text. A key challenge in this task is to efficiently learn effective graph representations. Previously, Graph Convolution Networks (GCNs) were used to e