{"created":"2021-03-01T06:18:23.539635+00:00","id":1804,"links":{},"metadata":{"_buckets":{"deposit":"2b8c8f11-a2d2-4b31-8b45-3b965e4cb51a"},"_deposit":{"id":"1804","owners":[],"pid":{"revision_id":0,"type":"depid","value":"1804"},"status":"published"},"_oai":{"id":"oai:repository.dl.itc.u-tokyo.ac.jp:00001804"},"item_7_alternative_title_1":{"attribute_name":"\u305d\u306e\u4ed6\u306e\u30bf\u30a4\u30c8\u30eb","attribute_value_mlt":[{"subitem_alternative_title":"\u8907\u6570\u6587\u66f8\u81ea\u52d5\u8981\u7d04\u306b\u304a\u3051\u308b\u8981\u7d04\u6587\u306e\u4e26\u3073\u9806\u306b\u3088\u308b\u4e00\u8cab\u6027\u5411\u4e0a\u306b\u95a2\u3059\u308b\u7814\u7a76"}]},"item_7_biblio_info_7":{"attribute_name":"\u66f8\u8a8c\u60c5\u5831","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2007-02-02","bibliographicIssueDateType":"Issued"},"bibliographic_titles":[{}]}]},"item_7_date_granted_25":{"attribute_name":"\u5b66\u4f4d\u6388\u4e0e\u5e74\u6708\u65e5","attribute_value_mlt":[{"subitem_dategranted":"2007-03"}]},"item_7_description_5":{"attribute_name":"\u6284\u9332","attribute_value_mlt":[{"subitem_description":"The problem of extracting salient information to include in a summary has been researched extensively in the field of automatic text summarization. However, coherent arrangement of the extracted information has received little attention. Specially, in the case of extractive multi-document text summarization, sentences that convey important information are selected from a set of documents. There is no guarantee that this set of extracted sentences will form a coherent summary by itself. The order of presentation of information is an important factor that affects the coherence of a summary. This thesis focuses on the problem of automatically generating a coherent summary from a given set of documents by ordering the extracted sentences. I propose two different approaches to this problem: a pair-wise sentence comparison approach and a bottom-up text structuring approach. The pair-wise sentence comparison approach first compares all possible pairs of sentences and decides partial orderings between the two sentences in pairs. It then creates a total ordering that optimizes a certain function. In the bottom-up text structuring approach, I define four criteria for sentence ordering: chronology, topical-closeness, precedence and succedence. I then use support vector machines to integrate these four different criteria to compute the strength of association between two sentences. For training I use a set of manually ordered summaries. A hierarchical text clustering algorithm is used to produce a total ordering of sentences. I begin by ordering the pair of sentences that has the highest strength of association. I then repeatedly order the two segments of texts with the maximum association strength until a single segment with all sentences ordered is formed. I compare the sentence orderings produced by the proposed algorithm against manually ordered summaries using various rank correlation measures. Moreover, I perform a subjective grading of the generated summaries. Both automatic evaluation and subjective grading suggest that the proposed sentence ordering algorithms significantly outperforms all existing sentence ordering methods for multi-document summarization. Moreover, I investigate the problem of automatically evaluating a sentence ordering for its coherence and propose Average Continuity as an automatic evaluation measure for this task. The proposed automatic evaluation measure reports a high correlation with human ratings.","subitem_description_type":"Abstract"}]},"item_7_full_name_3":{"attribute_name":"\u8457\u8005\u5225\u540d","attribute_value_mlt":[{"nameIdentifiers":[{"nameIdentifier":"5600","nameIdentifierScheme":"WEKO"}],"names":[{"name":"\u30dc\u30c3\u30ec\u30fc\u30ac\u30e9, \u30c0\u30cc\u30b7\u30ab"}]}]},"item_7_select_21":{"attribute_name":"\u5b66\u4f4d","attribute_value_mlt":[{"subitem_select_item":"master"}]},"item_7_subject_13":{"attribute_name":"\u65e5\u672c\u5341\u9032\u5206\u985e\u6cd5","attribute_value_mlt":[{"subitem_subject":"007","subitem_subject_scheme":"NDC"}]},"item_7_text_24":{"attribute_name":"\u7814\u7a76\u79d1\u30fb\u5c02\u653b","attribute_value_mlt":[{"subitem_text_value":"\u60c5\u5831\u7406\u5de5\u5b66\u7cfb\u7814\u7a76\u79d1\u96fb\u5b50\u60c5\u5831\u5b66\u5c02\u653b"}]},"item_7_text_27":{"attribute_name":"\u5b66\u4f4d\u8a18\u756a\u53f7","attribute_value_mlt":[{"subitem_text_value":"\u4fee\u7b2c\u53f7"}]},"item_7_text_4":{"attribute_name":"\u8457\u8005\u6240\u5c5e","attribute_value_mlt":[{"subitem_text_value":"\u5927\u5b66\u9662\u60c5\u5831\u7406\u5de5\u5b66\u7cfb\u7814\u7a76\u79d1\u96fb\u5b50\u60c5\u5831\u5b66\u5c02\u653b"}]},"item_creator":{"attribute_name":"\u8457\u8005","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Bollegala, Danushka"}],"nameIdentifiers":[{"nameIdentifier":"5599","nameIdentifierScheme":"WEKO"}]}]},"item_files":{"attribute_name":"\u30d5\u30a1\u30a4\u30eb\u60c5\u5831","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2017-05-31"}],"displaytype":"detail","filename":"Bollegala.pdf","filesize":[{"value":"541.4 kB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"Bollegala.pdf","url":"https://repository.dl.itc.u-tokyo.ac.jp/record/1804/files/Bollegala.pdf"},"version_id":"3b1e3c14-b8f4-42a1-bd09-64f42d8f9226"}]},"item_keyword":{"attribute_name":"\u30ad\u30fc\u30ef\u30fc\u30c9","attribute_value_mlt":[{"subitem_subject":"multi-document summarization","subitem_subject_scheme":"Other"},{"subitem_subject":"sentence ordering","subitem_subject_scheme":"Other"},{"subitem_subject":"text coherence","subitem_subject_scheme":"Other"},{"subitem_subject":"machine learning","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"\u8a00\u8a9e","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"\u8cc7\u6e90\u30bf\u30a4\u30d7","attribute_value_mlt":[{"resourcetype":"thesis","resourceuri":"http://purl.org/coar/resource_type/c_46ec"}]},"item_title":"IMPROVING COHERENCE IN MULI-DOCUMENT SUMMARIZATION THROUGH PROPER ORDERING OF SENTENCES","item_titles":{"attribute_name":"\u30bf\u30a4\u30c8\u30eb","attribute_value_mlt":[{"subitem_title":"IMPROVING COHERENCE IN MULI-DOCUMENT SUMMARIZATION THROUGH PROPER ORDERING OF SENTENCES"}]},"item_type_id":"7","owner":"1","path":["9/233/234","34/105/262"],"pubdate":{"attribute_name":"\u516c\u958b\u65e5","attribute_value":"2011-08-08"},"publish_date":"2011-08-08","publish_status":"0","recid":"1804","relation_version_is_last":true,"title":["IMPROVING COHERENCE IN MULI-DOCUMENT SUMMARIZATION THROUGH PROPER ORDERING OF SENTENCES"],"weko_creator_id":"1","weko_shared_id":null},"updated":"2021-03-02T08:14:50.644673+00:00"}