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The influence of income on medical school admissions in ...
The socioeconomic status of applicants to Canadian medical schools has been understudied in the past two decades. Institutional efforts have been made to address the lack of socioeconomic diversity across Canada during this time. We investigated the income characteristics of medical school applicants, as well as the relationship between applicant income and offer of admission, to characterize ...
Electronics | Free Full-Text | Incorporating External ...
In addition to the graph-based algorithms, unsupervised text cluster methods such as K-means and K-medoids are also used in document summarization tasks, and the sentences selected by these methods are independent of each other. In this paper, both the popular Textrank graph model and the K-means cluster algorithm are used in our approach.
[PDF] Unsupervised Summarization by Jointly Extracting ...
We present RepRank, an unsupervised graphbased ranking model for extractive multidocument summarization in which the similarity between words, sentences, and word-tosentence can be estimated by the distances between their vector representations in a unified vector space. In order to obtain desirable representations, we propose a self-attention based learning method that represent a sentence by ...
Unsupervised Summarization by Jointly Extracting Sentences ...
We present RepRank, an unsupervised graph-based ranking model for extractive multi-document summarization in which the similarity between words, sentences, and word-to-sentence can be estimated by the distances between their vector representations in a unified vector space. In order to obtain desirable representations, we propose a self-attention based learning method that represent a sentence ...
Electronics | Free Full-Text | Incorporating External ...
Supervised neural network models have achieved outstanding performance in the document summarization task in recent years. However, it is hard to get enough labeled training data with a high quality for these models to generate different types of summaries in reality. In this work, we mainly focus on improving the performance of the popular unsupervised Textrank algorithm that requires no ...
arXiv:2009.07481v1 [cs.CL] 16 Sep 2020
and paste based text summarization. In NAACL. Alex Kulesza and Ben Taskar. 2012. Determinantal point processes for machine learning. Foundations and Trends in Machine Learning, 1:123–286. Chen Li, Yang Liu, and Lin Zhao. 2015. Using exter-nal resources and joint learning for Bigram weight-ing in ILP-based multi-document summarization. In NAACL....
Somun: entity-centric summarization incorporating pre ...
Text summarization resolves the issue of capturing essential information from a large volume of text data. Existing methods either depend on the end-to-end models or hand-crafted preprocessing steps. In this study, we propose an entity-centric summarization method which extracts named entities and produces a small graph with a dependency parser. To extract entities, we employ well-known pre ......
Diversity-Based Generalization for Neural Unsupervised ...
02/25/20 - Domain adaptation approaches seek to learn from a source domain and generalize it to an unseen target domain. At present, the stat...
A Nodes' Evolution Diversity Inspired Method to Detect ...
This research focuses on graph-based methods and proposes a universal method for generalized social networks. Different from the existing graph-based methods that summarize a number of structural features, the proposed nodes' evolution diversity inspired method ( NEDM ) detects anomalies in dynamic social networks from the perspective of ...
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