| With the rapid development of new media,online news shows a tendency ofspring,people are faced with difficulties in information selection,information overload and other issues.A large amount of redundant information on the one handincrease the time ofreadinginformation,on the other hand can also increase the difficulties offindingand understandinghot topics.The popularity of mobile devices is driven to produce a large number of news clients,the characteristicofthese mobile devices is smallsc reens,while large amount of information can't display in a single screen,and split to other screen will cause inconvenience to read.Therefore,automatic news reductiontechnology emerges naturally,news reduction can gain not only simple and clear,depth and attitude of content resources,and can promote the quality ofthe network news,realizes the network news value-added reading.This paper launches a series of studies on the key technology of news headline compression and news reduction.In view of the news headlines,by means of word alignment the method based on the combination of rules and statistics is applied to compress.For the news text we propose the framework of sentence compression and sentences selection method to generate news text reduction results.In this framework,the paper firstly appliesmulti-feature fusion forsummary sentence pre-selection,which is produced by extractingthe most representative sentences based on sentence weight of multi-feature.Then for the pre-selected summary sentences,this paper puts forward a kind of heuristic sentence compression algorithm based on keywords,sentence compression technology is applied to generate the intermediate layer of source sentence compression variants.Finally the method removesredundancy by integer linear programming algorithm to select the most informative and the final news summary.In terms of evaluation,the paper presents a summarization evaluation method on the basis of sentence compression.The evaluation method through importance of information,grammaticality and compression ratio to score the compressed sentence,then combinesthe compressed sentence scores with the summary estimating to calculate accuracy,recall and F-value are 79.26%,76.32% and 77.76% respectively.The experimental results show that the news reductionalgorithm proposed in this paper is feasible,which can automatically generate a coherent and symbolic high-density news summary for the given news articles. |
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