Adaptive Summaries: A Personalized Concept-based Summarization Approach
  by Learning from Users' Feedback
  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.
