新闻视频中基于主持人识别的新闻故事探测

新闻视频由一个个内容相互独立的新闻故事组成。新闻故事探测是新闻视频浏览、基于内容检索等操作的前提。该文根据新闻视频的特殊结构和新闻节目主持人固定的特征，采用基于人脸检测的主持人镜头识别和基于语音的主持人识别来分割新闻视频中的新闻故事。实验表明，该方法能准确地探测出新闻视频中的新闻故事。     

网络环境下的新闻自动发布与管理系统

本文介绍了一种新闻自动发布与管理系统，并详细介绍了新闻标题和新闻内容页面的自动生成方法。     

一个基于内容的新闻视频浏览和查询系统:NewsBR

提出了一个基于内容的新闻视频浏览和查询系统NewsBR，这个系统是建立在非常准确的新闻故事分段和主题字幕文本提取之上的，它的主要特征包括：基于类别的新闻故事浏览，基于关键帧的视频摘要和基于关键词的新闻故事查询，本文详细讲述了新闻故事的分段，主题字幕文本的提取和在此之上的基于内容的视频浏览和查询，这个系统对于全面了解新闻视频的内容很有帮助且行之有效．     

互联网新闻信息服务安全评估工作发展研究

近年来，我国互联网新闻信息服务行业发展迅猛，微博、微信、APP等互联网新信息技术、新媒体应用的出现和普及，网络上的内容丰富且多样，很大程度满足了人们的日常网络需求，但同时一些虚假的、造谣的新闻内容也出现在互联网中，有一些团体或个人在互联网上进行新闻传播或转发过程中，对传播或转发的新闻内容进行恶意修改甚至虚构内容，这种行为使得网络用户的合法权益被肆意侵害，所以亟需尽快完善相关法律法规，落实责任，对互联网上的信息加强规范。     

基于内容的新闻视频检索技术研究

新闻视频的检索具有较大的实用意义。本文结合新闻视频的层次结构，从具体的每一步骤对现有的基于内容的新闻视频检索的常用技术，尤其是关于利用音视特征来进行新闻单元分割，进行了总结和探讨比较，指出了目前研究中存在的主要问题并提出了今后的研究方向。     

基于Web的新闻自动发布系统的设计和实现

介绍新闻自动发布系统的设计方法,基中主要说明如何添加和管理每天的新闻内容,把新闻代码插入到相关的页面,显示每条新闻的详细内容和相关新闻的标题,以及对新闻进行检索。     

虚假新闻检测技术的应用

虚假新闻在社交媒体上的广泛传播,会对个人和社会产生极其负面的影响。社交媒体上的虚假新闻检测技术成为重点的研究对象。本文总结现有研究,对虚假新闻检测技术进行了全面的概述,内容包括虚假新闻定义、虚假新闻检测技术分类、评价指标和代表性数据集。     

新闻与美学的关系探析

新闻宣传与传播是当今社会必不可少的信息沟通与交流的渠道,同时,要新闻及新闻宣传写作中能够及时地体现美学价值是新闻写作和宣传中一个重要的内容。对此,本文对新闻与美学的关联性进行了探讨,认为撰写新闻一要声情要并茂;二是新闻现场要细节化;三是新闻表达要多样化,通过这些体现出新闻学与美学的关系,使新闻更加多姿多彩,引人入胜,进而达到新闻宣传的目的。     

基于Ajax和Smarty的新闻管理系统的设计与实现

本文通过采用Ajax和Smarty模板技术来设计新闻管理系统,实现新闻内容的静态化,以提高访问速度,并利用Ajax异步请求方法向服务器获取数据集,实现信息的实时性.     

基于新闻网页主题要素的网页去重方法研究

网页检索结果中,用户经常会得到内容相同的冗余页面。提出了一种通过新闻主题要素学习新闻内容的新闻网页去重算法。该方法的基本思想是：首先,抽取新闻要素中关于事件发生的时间和地点短语;然后,通过抽取的时间和地点短语抽取新闻的内容;最终,根据学习的新闻内容通过计算它们的相似度来判断新闻网页的重复度。实验结果表明,该方法能够完成针对新闻内容的新闻网页的去重,并得到较高的查全率和查准率。     

基于卷积神经网络与篇章结构的足球新闻自动生成方法

当前的足球比赛新闻通常是由专家或记者手工撰写的,足球比赛新闻的手工写作既费时又低效。随着在线直播平台与社交媒体的流行,体育网络直播脚本大幅增加,但网络直播脚本通常只记载一场比赛的流水,具有冗长且重点模糊的特性,不适宜于赛后直接阅读。为了解决以上问题,在比赛之后,可以基于直播脚本撰写和发布足球比赛新闻。因此,该文提出一种从网络直播脚本直接生成足球比赛新闻的方法。该方法基于卷积神经网络和足球新闻篇章结构,从足球比赛过程中的多个时间段提取出已发生的重要事件,进而抽取相关句子来生成足球新闻,同时,该方法还会针对比赛评价生成一个简短总结。实验结果表明,使用该方法从网络直播脚本生成足球新闻是可行的。     

Query-Free News Search

Many daily activities present information in the form of a stream of text, and often people can benefit from additional information on the topic discussed. TV broadcast news can be treated as one such stream of text; in this paper we discuss finding news articles on the web that are relevant to news currently being broadcast. We evaluated a variety of algorithms for this problem, looking at the impact of inverse document frequency, stemming, compounds, history, and query length on the relevance and coverage of news articles returned in real time during a broadcast. We also evaluated several postprocessing techniques for improving the precision, including reranking using additional terms, reranking by document similarity, and filtering on document similarity. For the best algorithm, 84–91% of the articles found were relevant, with at least 64% of the articles being on the exact topic of the broadcast. In addition, a relevant article was found for at least 70% of the topics.     

A text summary-based method to detect new events from streams of online news articles

New event detection (NED), which is crucial to firms’ environmental surveillance, requires timely access to and effective analysis of live streams of news articles from various online sources. These news articles, available in unprecedent frequency and quantity, are difficult to sift through manually. Most of existing techniques for NED are full-text-based; typically, they perform full-text analysis to measure the similarity between a new article and previous articles. This full-text-based approach is potentially ineffective, because a news article often contains sentences that are less relevant to define the focal event being reported and the inclusion of these less relevant sentences into the similarity estimation can impair the effectiveness of NED. To address the limitation of the full-text-based approach and support NED more effectively and efficiently, this study proposes and develops a summary-based event detection method that first selects relevant sentences of each article as a summary, then uses the resulting summaries to detect new events. We empirically evaluate our proposed method in comparison with some prevalent full-text-based techniques, including a vector space model and two deep-learning-based models. Our evaluation results confirm that the proposed method provides greater utilities for detecting new events from online news articles. This study demonstrates the value and feasibility of the text summarization approach for generating news article summaries for detecting new events from live streams of online news articles, proposes a new method more effective and efficient than the benchmark techniques, and contributes to NED research in several important ways.     

基于宏微观重要性判别模型的时序多文档文摘

时序多文档文摘是针对新闻领域跨时段的相关文档集,即系列新闻报道进行问题无关的、抽取式文摘.根据系列新闻报道不同细节层次的时序特性.提出一种基于宏微观重要性判别模型的内容选择方法.从宏观和微观角度挖掘信息随着时间进化的时序特性,以指导时序多文档文摘的内容选择.首先通过宏观模型确定重要的时间点,然后通过微观模型在重要的时间点选择重要的句子,从而更有效地获取文摘.实验证明该方法是有效的.     

“TV no longer commands our full attention”: Effects of second-screen viewing and task relevance on cognitive load and learning from news

Second-screen viewing—the use of smartphones, tablets, and laptops while watching television—has increased dramatically in the last few years. Using multiple resource theory and threaded cognition theory, this study investigated the effects of second-screen viewing on cognitive load, factual recall and comprehension of news. Second, we examined the effects of relevant (i.e., looking up information related to the news story) and irrelevant (i.e., looking up information unrelated to the story) second-screen viewing on learning from news. Results from an experiment (N = 85) showed that second-screen viewing led to lower factual recall and comprehension of news content than single-screen viewing. These effects were mediated by cognitive load: second-screen viewing led to a higher cognitive load than single-screen viewing, with higher cognitive load, in turn, leading towards lower factual recall and comprehension of news content. Contrary to our expectations, we found no statistically significant differences between effects of relevant and irrelevant second-screen viewing.     

An intelligent news recommender agent for filtering and categorizing large volumes of text corpus

This article presents an intelligent news recommender agent (INRA), which can be used to filter news articles as well as to recommend relevant news for individual user automatically. Three specific objectives underlie the presentation of the intelligent news recommender agent in this study. The first is to describe the basic architecture of this approach, and the second is to show the design of the fuzzy hierarchical mixture of the expert model for text categorization. The third and more elaborate goal is to show that the proposed system is able to perform a news‐recommending process. We show this approach with standard benchmark examples of the Reuters‐21578 in order to verify the effectiveness of news recommending. © 2004 Wiley Periodicals, Inc.     

User comments for news recommendation in forum-based social media

News recommendation and user interaction are important features in many Web-based news services. The former helps users identify the most relevant news for further information. The latter enables collaborated information sharing among users with their comments following news postings. This research is intended to marry these two features together for an adaptive recommender system that utilizes reader comments to refine the recommendation of news in accordance with the evolving topic. This then turns the traditional “push-data” type of news recommendation to “discussion” moderator that can intelligently assist online forums. In addition, to alleviate the problem of recommending essentially identical articles, the relationship (duplicate, generalization, or specialization) between recommended news articles and the original posting is investigated. Our experiments indicate that our proposed solutions provide an improved news recommendation service in forum-based social media.     

基于突发事件新闻标题的文本过滤模型

文本过滤是指从大量的文本中寻找满足用户需求的文本的过程。以互联网上下载的突发事件新闻文本为研究背景,提出了基于新闻标题的文本过滤模型,根据示例文本构建标题过滤模板,采用基于关键字的过滤方法对突发事件新闻文本进行过滤。其特点是实现简单,过滤速度快,有一定的实际作用。     

基于特征检索的中文短评生成

针对从大数据评论语料库中检索出与新闻主题相关且含有情感倾向性的中文评论的研究较少的问题,研究在不同新闻粒度下的特征检索方法,从中文评论语料库中检索生成评论。采用主题特征检索的方法检索出与新闻主题特征相关的评论;采用情感特征融合的检索方法从主题特征检索的结果中生成所需情感倾向性的评论。实验结果表明,在新闻标题粒度下生成评论的主题相关性最高;采用主题特征融合的检索方法和情感特征融合的检索方法比单一检索方法生成准确率更高。     

DeNews-A personalized news system

Many commercial systems and much R&D work are aimed at easing the information explosion problem resulting from the advent of the Information Superhighway. One solution is to personalize the information to the specific interests of a user. A personalized news system named DeNews has been developed to track multilingual news sources, filter the relevant news articles, learn about the users's interests, sort news articles into defined classes, deliver them in full or summarized form, and translate them to a specific language. Many advanced text and natural language processing techniques are required to implement these functions and to facilitate the multilingual aspect of DeNews and the overall management of the huge amount of news articles. It is envisaged that the technology developed with DeNews will be especially suitable in a domain-specific corporate business environment, where accurate and timely information is critical.