{"id":"https://openalex.org/W268791909","doi":"https://doi.org/10.21437/interspeech.2012-615","title":"Integrating intra-speaker topic modeling and temporal-based inter-speaker topic modeling in random walk for improved multi-party meeting summarization","display_name":"Integrating intra-speaker topic modeling and temporal-based inter-speaker topic modeling in random walk for improved multi-party meeting summarization","publication_year":2012,"publication_date":"2012-09-09","ids":{"openalex":"https://openalex.org/W268791909","doi":"https://doi.org/10.21437/interspeech.2012-615","mag":"268791909"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2012-615","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2012-615","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2012","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://figshare.com/articles/journal_contribution/Integrating_Intra-Speaker_Topic_Modeling_and_Temporal-Based_Inter-Speaker_Topic_Modeling_in_Random_Walk_for_Improved_Multi-Party_Meeting_Summarization/6473453","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076610826","display_name":"Yun-Nung Chen","orcid":"https://orcid.org/0000-0003-1777-3942"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yun-Nung Chen","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, United States","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085262529","display_name":"Florian Metze","orcid":"https://orcid.org/0000-0002-6663-8600"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Florian Metze","raw_affiliation_strings":["Carnegie Mellon University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":1.0407,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.73686304,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2346","last_page":"2349"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12031","display_name":"Speech and dialogue systems","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.837780773639679},{"id":"https://openalex.org/keywords/automatic-summarization","display_name":"Automatic summarization","score":0.777484655380249},{"id":"https://openalex.org/keywords/utterance","display_name":"Utterance","score":0.6759785413742065},{"id":"https://openalex.org/keywords/speaker-diarisation","display_name":"Speaker diarisation","score":0.5744409561157227},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5171322226524353},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.5055532455444336},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4985175132751465},{"id":"https://openalex.org/keywords/topic-model","display_name":"Topic model","score":0.47906622290611267},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4164814054965973},{"id":"https://openalex.org/keywords/latent-semantic-analysis","display_name":"Latent semantic analysis","score":0.41636621952056885},{"id":"https://openalex.org/keywords/speaker-recognition","display_name":"Speaker recognition","score":0.39437225461006165},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3933106064796448}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.837780773639679},{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.777484655380249},{"id":"https://openalex.org/C2775852435","wikidata":"https://www.wikidata.org/wiki/Q258403","display_name":"Utterance","level":2,"score":0.6759785413742065},{"id":"https://openalex.org/C149838564","wikidata":"https://www.wikidata.org/wiki/Q7574248","display_name":"Speaker diarisation","level":3,"score":0.5744409561157227},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5171322226524353},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5055532455444336},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4985175132751465},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.47906622290611267},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4164814054965973},{"id":"https://openalex.org/C170133592","wikidata":"https://www.wikidata.org/wiki/Q1806883","display_name":"Latent semantic analysis","level":2,"score":0.41636621952056885},{"id":"https://openalex.org/C133892786","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker recognition","level":2,"score":0.39437225461006165},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3933106064796448},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.21437/interspeech.2012-615","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2012-615","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2012","raw_type":"proceedings-article"},{"id":"pmh:oai:repository.cmu.edu:lti-1104","is_oa":false,"landing_page_url":"http://repository.cmu.edu/lti/112","pdf_url":null,"source":{"id":"https://openalex.org/S7407050927","display_name":"KiltHub Repository","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Language Technologies Institute","raw_type":"text"},{"id":"pmh:doi:10.1184/r1/6473453","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Journal contribution"},{"id":"pmh:oai:figshare.com:article/6473453","is_oa":true,"landing_page_url":"https://figshare.com/articles/journal_contribution/Integrating_Intra-Speaker_Topic_Modeling_and_Temporal-Based_Inter-Speaker_Topic_Modeling_in_Random_Walk_for_Improved_Multi-Party_Meeting_Summarization/6473453","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"},{"id":"doi:10.1184/r1/6473453.v1","is_oa":true,"landing_page_url":"https://doi.org/10.1184/r1/6473453.v1","pdf_url":null,"source":{"id":"https://openalex.org/S7407050927","display_name":"KiltHub Repository","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"JournalArticle"}],"best_oa_location":{"id":"pmh:oai:figshare.com:article/6473453","is_oa":true,"landing_page_url":"https://figshare.com/articles/journal_contribution/Integrating_Intra-Speaker_Topic_Modeling_and_Temporal-Based_Inter-Speaker_Topic_Modeling_in_Random_Walk_for_Improved_Multi-Party_Meeting_Summarization/6473453","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.7400000095367432,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1483790978","https://openalex.org/W1854214752","https://openalex.org/W1880262756","https://openalex.org/W1971307269","https://openalex.org/W2002890640","https://openalex.org/W2023170106","https://openalex.org/W2097321121","https://openalex.org/W2099160590","https://openalex.org/W2107743791","https://openalex.org/W2111928519","https://openalex.org/W2131780215","https://openalex.org/W2145541974","https://openalex.org/W2147730187","https://openalex.org/W2152789372","https://openalex.org/W2154652894","https://openalex.org/W2169384404","https://openalex.org/W2171515436","https://openalex.org/W2408289997","https://openalex.org/W3101913037","https://openalex.org/W4233135949"],"related_works":["https://openalex.org/W2206035908","https://openalex.org/W4247736853","https://openalex.org/W2162158162","https://openalex.org/W1493012537","https://openalex.org/W1999004162","https://openalex.org/W2175373321","https://openalex.org/W2125642021","https://openalex.org/W1521049138","https://openalex.org/W2938358845","https://openalex.org/W2997340161"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"an":[3],"improved":[4],"approach":[5],"of":[6,34],"summarization":[7],"for":[8,94,120],"spoken":[9],"multi-party":[10],"interaction,":[11],"in":[12,20],"which":[13,52],"intra-speaker":[14,65,105],"and":[15,37,75,96,106],"inter-speaker":[16,76,107],"topics":[17,66,70,77,82],"are":[18],"modeled":[19],"a":[21,32],"graph":[22,36],"constructed":[23],"with":[24],"topical":[25,54],"relations.":[26],"Each":[27],"utterance":[28],"is":[29,43,53],"represented":[30],"as":[31],"node":[33],"the":[35,38,46,49,69,72,81,84,113,118],"edge":[39],"between":[40,48],"two":[41,50],"nodes":[42],"weighted":[44],"by":[45,57,67,78],"similarity":[47,55],"utterances,":[51],"evaluated":[56],"probabilistic":[58],"latent":[59],"semantic":[60],"analysis":[61],"(PLSA).":[62],"We":[63,91],"model":[64],"sharing":[68,80],"from":[71,83],"same":[73],"speaker":[74],"partially":[79],"adjacent":[85],"utterances":[86,115],"based":[87],"on":[88],"temporal":[89],"information.":[90],"did":[92],"experiments":[93,102],"ASR":[95],"manual":[97],"transcripts.":[98],"For":[99],"both":[100],"transcripts,":[101],"showed":[103],"combining":[104],"topic":[108],"modeling":[109],"can":[110],"help":[111],"include":[112],"important":[114],"to":[116],"offer":[117],"improvement":[119],"summarization.":[121]},"counts_by_year":[{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":3},{"year":2013,"cited_by_count":3},{"year":2012,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
