Search

Automatic related work generation is a new challenge in multi-document scientific summarization focusing on refining a related work section for a given scientific paper. In this paper, we propose a brand new framework ToC-RWG for related work generation by incorporating topic model and citation information. We present an unsupervised generative probabilistic model, called QueryTopicSum, which utilizes a LDA-style model to characterize the generative process of both the scientific paper and its reference papers. We also take advantage of citations of reference papers to identify Cited Text Spans (CTS) from reference papers. This approach provides us with a perspective of annotating the importance of the reference papers from the academic community. With QueryTopicSum and the identified CTS as candidate sentences, an optimization framework based on minimizing KL divergence is exerted to select the most representative sentences for related work generation. Our evaluation results on a set of 50 scientific papers along with their corresponding reference papers show that ToC-RWG achieves a considerable improvement over generic multi-document summarization and scientific summarization baselines.

Our limited understanding of the hybrid sterility (HS) mechanism in Asian–African rice hybrids hampers utilization of the interspecific heterosis for rice production. Here, the authors identify S1-mediated HS-related tripartite gamete killer-protector system, and explore their evolutionary relationship.

The article focuses on improving Knowledge Tracing (KT) in e-learning systems by addressing the Skill Switching Phenomenon (SSP). Topics include the challenges posed by SSP, the limitations of existing deep learning-based methods, and the proposed Memory-augmented Attentive Network (MAN) model to enhance knowledge state prediction.