AsU-OSum: Aspect-augmented unsupervised opinion summarization

Opinion summarization can facilitate user’s decision-making by mining the salient review information. However, due to the lack of sufficient annotated data, most of the early works are based on extractive methods, which restricts the performance of opinion summarization. In this work, we aim to improve the informativeness of opinion summarization to provide better guidance to users. We consider the setting with only reviews without corresponding summaries, and propose an aspect-augmented model for unsupervised abstractive opinion summarization, denoted as AsU-OSum. We first employ an aspect-based sentiment analysis system to extract opinion phrases from reviews. Then, we construct a heterogeneous graph consisting of reviews and opinion clusters as nodes, which is used to enhance the Transformer-based encoder–decoder framework. Furthermore, we design a novel cascaded attention mechanism to prompt the decoder to pay more attention to the aspects that are more likely to appear in summary. During training, we introduce a sentiment accuracy reward that further enhances the learning ability of our model. We conduct comprehensive experiments on the Yelp, Amazon, and Rotten Tomatoes datasets. Automatic evaluation results show that our model is competitive and performs better than the state-of-the-art (SOTA) models on some ROUGE metrics. Human evaluation results further verify that our model can generate more informative summaries and reduce redundancy.     

An extract-then-abstract based method to generate disaster-news headlines using a DNN extractor followed by a transformer abstractor

Generating news headlines has been one of the predominant problems in Natural Language Processing research. Modern transformer models, if fine-tuned, can present a good headline with attention to all the parts of a disaster-news article. A disaster-news headline generally focuses on the event, its effect, and the major impacts, which a transformer model lacks when generating the headline. The extract-then-abstract based method proposed in this article improves the performance of a state-of-the-art transformer abstractor to generate a good-quality disaster-news headline. In this work, a Deep Neural Network (DNN) based sentence extractor and a transformer-based abstractive summarizer work sequentially to generate a headline. The sentence extraction task is formulated as a binary classification problem where the DNN model is trained to generate two binary labels: one corresponding to the sentence similarity with ground truth headlines and the other corresponding to the presence of disaster and its impact related information in the sentence. The transformer model generates the headline from the sentences extracted by the DNN. ROUGE scores of the headlines generated using the proposed method are found to be better than the scores of the headlines generated directly from the original documents. The highest ROUGE 1, 2, and 3 score improvements are observed in the case of the Text-To-Text Transfer Transformer (T5) model by 17.85%, 38.13%, and 21.01%, respectively. Such improvements suggest that the proposed method can have a high utility for finding effective headlines from disaster related news articles.     

MRC-Sum: An MRC framework for extractive summarization of academic articles in natural sciences and medicine

Extractive summarization for academic articles in natural sciences and medicine has attracted attention for a long time. However, most existing extractive summarization models often process academic articles with sentence classification models, which are hard to produce comprehensive summaries. To address this issue, we explore a new view to solve the extractive summarization of academic articles in natural sciences and medicine by taking it as a question-answering process. We propose a novel framework, MRC-Sum, where the extractive summarization for academic articles in natural sciences and medicine is cast as an MRC (Machine Reading Comprehension) task. To instantiate MRC-Sum, article-summary pairs in the summarization datasets are firstly reconstructed into (Question, Answer, Context) triples in the MRC task. Several questions are designed to cover the main aspects (e.g. Background, Method, Result, Conclusion) of the articles in natural sciences and medicine. A novel strategy is proposed to solve the problem of the non-existence of the ground truth answer spans. Then MRC-Sum is trained on the reconstructed datasets and large-scale pre-trained models. During the inference stage, four answer spans of the predefined questions are given by MRC-Sum and concatenated to form the final summary for each article. Experiments on three publicly available benchmarks, i.e., the Covid, PubMed, and arXiv datasets, demonstrate the effectiveness of MRC-Sum. Specifically, MRC-Sum outperforms advanced extractive summarization baselines on the Covid dataset and achieves competitive results on the PubMed and arXiv datasets. We also propose a novel metric, COMPREHS, to automatically evaluate the comprehensiveness of the system summaries for academic articles in natural sciences and medicine. Abundant experiments are conducted and verified the reliability of the proposed metric. And the results of the COMPREHS metric show that MRC-Sum is able to generate more comprehensive summaries than the baseline models.     

中国足球后备人才培养历史回顾与探讨

通过文献综述，对从新中国成立以来我国足球后备人才培养进行了阐述，对后备人才的培养机构及管理情况进行了分析和探讨，为展望中国足球未来发展提供理论依据和研究参考。     

豫东花鼓戏源流考

花鼓是中国民间歌舞的一种形式，商丘花鼓戏是河南省东部一种戏剧体裁，它源于何时，其发展形势如何无人进行研究，国内出版的词典和志书也没有关于它的文字记裁。“诗言志，歌咏言”商丘花鼓戏是豫东人民语言的延伸与发展，它的节奏、旋律及其所组成的曲调，都有其鲜明的地方特色，她是豫东地区人类发展史上遗留下来的一份宝贵文化遗产。为此就它的分布情况，演员与班社、花鼓戏音乐及其源流进行较为详细地介绍，并对它的形成与发展进行了逻辑的分析和探讨。     

结合学科特点和自身优势建立生物医学工程本科专业实验教学体系

生物医学工程是一门交叉性很强的学科。我校在该领域的科研优势主要在于生物医用材料及医疗器械方面。以此为基础，2003年建立了生物医学工程本科专业。为使学生通过本科阶段的学习，既能掌握该专业的综合知识，又具有较好的创新能力，我校结合自身科研优势，对建立有特色的实验教学体系进行了初步的研究和探索。该文介绍了此实验教学体系的目标和特色，介绍了实验教学内容和实验室的设置以及实验教师的配备情况。实验内容强调基础知识，注重交叉学科特点和体现自身特色。设置了生物医学实验室、生物电子实验室和医疗器械实验室。编写了相应的实验教材。     

生物医用金属材料的研究进展

介绍了医用金属材料目前的研究现状、性能和应用,指出了医用金属材料应用中目前存在的主要问题,阐述了近年来生物医用金属材料的新进展,提出镁及镁合金材料的应用是生物医用金属材料的发展方向。     

科学发展观研究综述

自科学发展观提出以来,理论界对科学发展观的基本内涵与精神实质,对科学发展观提出的时代背景与伟大意义以及如何贯彻和落实科学发展观进行了广泛深入的研究和阐释,形成了一系列研究成果。     

求导方法新探索

通过对两个求导例题的分析,归纳出求函数在某些特殊点导数的新的解法定理及推论,并举例说明它们的应用,最后对此求导方法进行总结。     

基于向量空间模型的文献相关性数据库的研究与实现*

探讨“相关性”的概念,简述文献相关性数据库的研究现状,提出基于词表和特征项提取的向量空间模型,并在此基础上设计、构建中国生物医学工程文献相关性数据库及其检索系统。