{"id":"https://openalex.org/W4405633734","doi":"https://doi.org/10.1109/mlnlp63328.2024.10800312","title":"Efficient Comprehension: PMTSS for Simplifying and Summarizing Scientific Literature","display_name":"Efficient Comprehension: PMTSS for Simplifying and Summarizing Scientific Literature","publication_year":2024,"publication_date":"2024-10-18","ids":{"openalex":"https://openalex.org/W4405633734","doi":"https://doi.org/10.1109/mlnlp63328.2024.10800312"},"language":"en","primary_location":{"id":"doi:10.1109/mlnlp63328.2024.10800312","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlnlp63328.2024.10800312","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 7th International Conference on Machine Learning and Natural Language Processing (MLNLP)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100575513","display_name":"Yuxuan Li","orcid":"https://orcid.org/0000-0002-0613-3969"},"institutions":[{"id":"https://openalex.org/I4210135483","display_name":"Beijing Institute of Graphic Communication","ror":"https://ror.org/03yg3v757","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210135483"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxuan Li","raw_affiliation_strings":["Beijing Institute of Graphic Communication,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Graphic Communication,Beijing,China","institution_ids":["https://openalex.org/I4210135483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100399257","display_name":"Han Zhang","orcid":"https://orcid.org/0000-0001-7072-2189"},"institutions":[{"id":"https://openalex.org/I4210135483","display_name":"Beijing Institute of Graphic Communication","ror":"https://ror.org/03yg3v757","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210135483"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Han Zhang","raw_affiliation_strings":["Beijing Institute of Graphic Communication,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Graphic Communication,Beijing,China","institution_ids":["https://openalex.org/I4210135483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102661962","display_name":"Dan Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210135483","display_name":"Beijing Institute of Graphic Communication","ror":"https://ror.org/03yg3v757","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210135483"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dan Jiang","raw_affiliation_strings":["Beijing Institute of Graphic Communication,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Graphic Communication,Beijing,China","institution_ids":["https://openalex.org/I4210135483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021748538","display_name":"Kejing Xiao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210135483","display_name":"Beijing Institute of Graphic Communication","ror":"https://ror.org/03yg3v757","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210135483"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kejing Xiao","raw_affiliation_strings":["Beijing Institute of Graphic Communication,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Graphic Communication,Beijing,China","institution_ids":["https://openalex.org/I4210135483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101573351","display_name":"Shaozhong Cao","orcid":"https://orcid.org/0000-0001-6949-1758"},"institutions":[{"id":"https://openalex.org/I4210135483","display_name":"Beijing Institute of Graphic Communication","ror":"https://ror.org/03yg3v757","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210135483"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaozhong Cao","raw_affiliation_strings":["Beijing Institute of Graphic Communication,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Graphic Communication,Beijing,China","institution_ids":["https://openalex.org/I4210135483"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100331004","display_name":"Xiang Li","orcid":"https://orcid.org/0000-0001-6968-5958"},"institutions":[{"id":"https://openalex.org/I4210135483","display_name":"Beijing Institute of Graphic Communication","ror":"https://ror.org/03yg3v757","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210135483"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiang Li","raw_affiliation_strings":["Beijing Institute of Graphic Communication,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Graphic Communication,Beijing,China","institution_ids":["https://openalex.org/I4210135483"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210135483"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.29608661,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9674000144004822,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9674000144004822,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10215","display_name":"Semantic Web and Ontologies","score":0.9253000020980835,"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":0.7057063579559326},{"id":"https://openalex.org/keywords/comprehension","display_name":"Comprehension","score":0.6087405681610107},{"id":"https://openalex.org/keywords/program-comprehension","display_name":"Program comprehension","score":0.41172054409980774},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.39529260993003845},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3606526255607605},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.2983454167842865},{"id":"https://openalex.org/keywords/software","display_name":"Software","score":0.11549264192581177},{"id":"https://openalex.org/keywords/software-system","display_name":"Software system","score":0.06827986240386963}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7057063579559326},{"id":"https://openalex.org/C511192102","wikidata":"https://www.wikidata.org/wiki/Q5156948","display_name":"Comprehension","level":2,"score":0.6087405681610107},{"id":"https://openalex.org/C2777561058","wikidata":"https://www.wikidata.org/wiki/Q2652119","display_name":"Program comprehension","level":4,"score":0.41172054409980774},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.39529260993003845},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3606526255607605},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.2983454167842865},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.11549264192581177},{"id":"https://openalex.org/C149091818","wikidata":"https://www.wikidata.org/wiki/Q2429814","display_name":"Software system","level":3,"score":0.06827986240386963}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlnlp63328.2024.10800312","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlnlp63328.2024.10800312","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 7th International Conference on Machine Learning and Natural Language Processing (MLNLP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W2534253848","https://openalex.org/W2926026721","https://openalex.org/W2963216553","https://openalex.org/W2968888988","https://openalex.org/W2970295111","https://openalex.org/W2970419734","https://openalex.org/W3034238904","https://openalex.org/W3034408878","https://openalex.org/W3042309530","https://openalex.org/W3104442086","https://openalex.org/W3166664235","https://openalex.org/W3174828871","https://openalex.org/W3184869503","https://openalex.org/W3208527886","https://openalex.org/W4385573391","https://openalex.org/W6604517304","https://openalex.org/W6676588247","https://openalex.org/W6682631176","https://openalex.org/W6732477580","https://openalex.org/W6755207826","https://openalex.org/W6761205521","https://openalex.org/W6769311223","https://openalex.org/W6769627184","https://openalex.org/W6771915120","https://openalex.org/W6776048684","https://openalex.org/W6778883912","https://openalex.org/W6797816506","https://openalex.org/W6839397204"],"related_works":["https://openalex.org/W2724855087","https://openalex.org/W2886906914","https://openalex.org/W2884362859","https://openalex.org/W1508879959","https://openalex.org/W2123179197","https://openalex.org/W2008149650","https://openalex.org/W1995506819","https://openalex.org/W2110798500","https://openalex.org/W2899084096","https://openalex.org/W4302570658"],"abstract_inverted_index":{"Text":[0,59],"summarization":[1,83,137],"aims":[2],"to":[3,19,48,91,158],"extract":[4],"key":[5],"information":[6],"from":[7],"lengthy":[8],"texts,":[9],"facilitating":[10],"rapid":[11],"comprehension":[12],"for":[13,27,58,67,82,177],"readers.":[14],"Additionally,":[15,130],"text":[16,90],"simplification":[17],"seeks":[18],"render":[20],"complex":[21],"terminology":[22],"into":[23,34],"more":[24,51],"simple":[25],"language":[26],"a":[28,63,167],"broader":[29],"audience.":[30],"This":[31],"study":[32],"delves":[33],"the":[35,55,71,78,85,103,119,127,133,136,172],"realm":[36],"of":[37,45,105,126,135,169],"lay":[38],"summarization,":[39],"which":[40],"involves":[41],"generating":[42],"simplified":[43,89],"summaries":[44],"scientific":[46],"articles":[47],"make":[49],"them":[50],"digestible.":[52],"We":[53],"present":[54],"Pre-trained":[56],"Model":[57],"Simplified":[60],"Summarization":[61],"(PMTSS),":[62],"systematic":[64],"framework":[65,153],"devised":[66],"this":[68,178],"purpose.":[69],"Initially,":[70],"backbone":[72],"model":[73,110,138],"was":[74,112],"determined":[75],"by":[76,139],"comparing":[77],"state-of-the-art":[79],"pretrained":[80],"models":[81,86,161],"since":[84],"could":[87],"generate":[88],"some":[92],"extent.":[93],"Extensive":[94],"experiments":[95],"and":[96,102,124,143],"comprehensive":[97,173],"evaluation":[98,164,174],"metrics":[99],"were":[100],"utilized":[101],"impact":[104],"different":[106],"input-output":[107],"lengths":[108],"on":[109,118,171],"performance":[111,134,156],"also":[113],"investigated.":[114],"Our":[115],"research":[116],"focused":[117],"relevance,":[120],"readability,":[121],"factual":[122],"accuracy,":[123],"simplicity":[125],"generated":[128],"text.":[129],"we":[131],"improved":[132],"employing":[140],"data":[141],"augmentation":[142],"intermediate":[144],"pretraining":[145],"techniques.":[146],"The":[147],"results":[148],"have":[149],"demonstrated":[150],"that":[151],"our":[152],"shows":[154],"superior":[155],"compared":[157],"reasonable":[159],"baseline":[160],"across":[162],"all":[163],"metrics,":[165],"achieving":[166],"score":[168],"35.85":[170],"metric":[175],"CSS":[176],"task.":[179]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}