News media profiling is helpful in preventing the spread of fake news at the source and maintaining a good media and news ecosystem.Most previous works only extract features and evaluate media from one dimension indep...News media profiling is helpful in preventing the spread of fake news at the source and maintaining a good media and news ecosystem.Most previous works only extract features and evaluate media from one dimension independently,ignoring the interconnections between different aspects.This paper proposes a novel news media bias and factuality profiling framework assisted by correlated features.This framework models the relationship and interaction between media bias and factuality,utilizing this relationship to assist in the prediction of profiling results.Our approach extracts features independently while aligning and fusing them through recursive convolu-tion and attention mechanisms,thus harnessing multi-scale interactive information across different dimensions and levels.This method improves the effectiveness of news media evaluation.Experimental results indicate that our proposed framework significantly outperforms existing methods,achieving the best performance in Accuracy and F1 score,improving by at least 1%compared to other methods.This paper further analyzes and discusses based on the experimental results.展开更多
多模态生成式摘要往往采用序列到序列(Seq2Seq)框架,目标函数在字符级别优化模型,根据局部最优解生成单词,忽略了摘要样本全局语义信息,使得摘要与多模态信息产生语义偏差,容易造成事实性错误。针对上述问题,提出一种基于语义相关性分...多模态生成式摘要往往采用序列到序列(Seq2Seq)框架,目标函数在字符级别优化模型,根据局部最优解生成单词,忽略了摘要样本全局语义信息,使得摘要与多模态信息产生语义偏差,容易造成事实性错误。针对上述问题,提出一种基于语义相关性分析的多模态摘要模型。首先,在Seq2Seq框架基础上对多模态摘要进行训练,生成语义多样性的候选摘要;其次,构建基于语义相关性分析的摘要评估器,从全局的角度学习候选摘要之间的语义差异性和真实评价指标ROUGE(Recall-Oriented Understudy for Gisting Evaluation)的排序模式,从而在摘要样本层面优化模型;最后,不依赖参考摘要,利用摘要评估器对候选摘要进行评价,使得选出的摘要与源文本在语义空间中尽可能相似。实验结果表明,在公开数据集MMSS上,相较于MPMSE(Multimodal Pointer-generator via Multimodal Selective Encoding)模型,所提模型在ROUGE-1、ROUGE-2、ROUGE-L评价指标上分别提升了3.17、1.21和2.24个百分点。展开更多
基金funded by“the Fundamental Research Funds for the Central Universities”,No.CUC23ZDTJ005.
文摘News media profiling is helpful in preventing the spread of fake news at the source and maintaining a good media and news ecosystem.Most previous works only extract features and evaluate media from one dimension independently,ignoring the interconnections between different aspects.This paper proposes a novel news media bias and factuality profiling framework assisted by correlated features.This framework models the relationship and interaction between media bias and factuality,utilizing this relationship to assist in the prediction of profiling results.Our approach extracts features independently while aligning and fusing them through recursive convolu-tion and attention mechanisms,thus harnessing multi-scale interactive information across different dimensions and levels.This method improves the effectiveness of news media evaluation.Experimental results indicate that our proposed framework significantly outperforms existing methods,achieving the best performance in Accuracy and F1 score,improving by at least 1%compared to other methods.This paper further analyzes and discusses based on the experimental results.
文摘多模态生成式摘要往往采用序列到序列(Seq2Seq)框架,目标函数在字符级别优化模型,根据局部最优解生成单词,忽略了摘要样本全局语义信息,使得摘要与多模态信息产生语义偏差,容易造成事实性错误。针对上述问题,提出一种基于语义相关性分析的多模态摘要模型。首先,在Seq2Seq框架基础上对多模态摘要进行训练,生成语义多样性的候选摘要;其次,构建基于语义相关性分析的摘要评估器,从全局的角度学习候选摘要之间的语义差异性和真实评价指标ROUGE(Recall-Oriented Understudy for Gisting Evaluation)的排序模式,从而在摘要样本层面优化模型;最后,不依赖参考摘要,利用摘要评估器对候选摘要进行评价,使得选出的摘要与源文本在语义空间中尽可能相似。实验结果表明,在公开数据集MMSS上,相较于MPMSE(Multimodal Pointer-generator via Multimodal Selective Encoding)模型,所提模型在ROUGE-1、ROUGE-2、ROUGE-L评价指标上分别提升了3.17、1.21和2.24个百分点。
