{"_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_36": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"subitem_text_value": "Thesis"}]}, "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", "download_preview_message": "", "file_order": 0, "filename": "Bollegala.pdf", "filesize": [{"value": "541.4 kB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 541400.0, "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"], "permalink_uri": "http://hdl.handle.net/2261/25847", "pubdate": {"attribute_name": "\u516c\u958b\u65e5", "attribute_value": "2011-08-08"}, "publish_date": "2011-08-08", "publish_status": "0", "recid": "1804", "relation": {}, "relation_version_is_last": true, "title": ["IMPROVING COHERENCE IN MULI-DOCUMENT SUMMARIZATION THROUGH PROPER ORDERING OF SENTENCES"], "weko_shared_id": null}