TY - GEN
T1 - Adaptive summaries
T2 - AIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events held in conjunction with 18th International Conference on Service-Oriented Computing, ICSOC 2020
AU - Ghodratnama, Samira
AU - Zakershahrak, Mehrdad
AU - Sobhanmanesh, Fariborz
PY - 2021
Y1 - 2021
N2 - Exploring the tremendous amount of data efficiently to make a decision, similar to answering a complicated question, is challenging with many real-world application scenarios. In this context, automatic summarization has substantial importance as it will provide the foundation for big data analytic. Traditional summarization approaches optimize the system to produce a short static summary that fits all users that do not consider the subjectivity aspect of summarization, i.e., what is deemed valuable for different users, making these approaches impractical in real-world use cases. This paper proposes an interactive concept-based summarization model, called Adaptive Summaries, that helps users make their desired summary instead of producing a single inflexible summary. The system learns from users’ provided information gradually while interacting with the system by giving feedback in an iterative loop. Users can choose either reject or accept action for selecting a concept being included in the summary with the importance of that concept from users’ perspectives and confidence level of their feedback. The proposed approach can guarantee interactive speed to keep the user engaged in the process. Furthermore, it eliminates the need for reference summaries, which is a challenging issue for summarization tasks. Evaluations show that Adaptive Summaries helps users make high-quality summaries based on their preferences by maximizing the user-desired content in the generated summaries.
AB - Exploring the tremendous amount of data efficiently to make a decision, similar to answering a complicated question, is challenging with many real-world application scenarios. In this context, automatic summarization has substantial importance as it will provide the foundation for big data analytic. Traditional summarization approaches optimize the system to produce a short static summary that fits all users that do not consider the subjectivity aspect of summarization, i.e., what is deemed valuable for different users, making these approaches impractical in real-world use cases. This paper proposes an interactive concept-based summarization model, called Adaptive Summaries, that helps users make their desired summary instead of producing a single inflexible summary. The system learns from users’ provided information gradually while interacting with the system by giving feedback in an iterative loop. Users can choose either reject or accept action for selecting a concept being included in the summary with the importance of that concept from users’ perspectives and confidence level of their feedback. The proposed approach can guarantee interactive speed to keep the user engaged in the process. Furthermore, it eliminates the need for reference summaries, which is a challenging issue for summarization tasks. Evaluations show that Adaptive Summaries helps users make high-quality summaries based on their preferences by maximizing the user-desired content in the generated summaries.
KW - Adaptive summaries
KW - Interactive summarization
KW - Multi-document summarization
KW - Personalized summaries
KW - Preference-based summaries
UR - http://www.scopus.com/inward/record.url?scp=85111388587&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-76352-7_29
DO - 10.1007/978-3-030-76352-7_29
M3 - Conference proceeding contribution
AN - SCOPUS:85111388587
SN - 9783030763510
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 281
EP - 293
BT - Service-Oriented Computing – ICSOC 2020 Workshops
A2 - Hacid, Hakim
A2 - Outay, Fatma
A2 - Paik, Hye-young
A2 - Alloum, Amira
A2 - Petrocchi, Marinella
A2 - Bouadjenek, Mohamed Reda
A2 - Beheshti, Amin
A2 - Liu, Xumin
A2 - Maaradji, Abderrahmane
PB - Springer, Springer Nature
CY - Cham, Switzerland
Y2 - 14 December 2020 through 17 December 2020
ER -