Information integration plays a pivotal role in biomedical studies by facilitating the combination and analysis of independent datasets from multiple studies, thereby uncovering valuable insights that might otherwise remain obscured due to the limited sample size in individual studies. However, sharing raw data from independent studies presents significant challenges, primarily due to the need to safeguard sensitive participant information and the cumbersome paperwork involved in data sharing. In this article, we first provide a selective review of recent methodological developments in information integration via empirical likelihood, wherein only summary information is required, rather than the raw data. Following this, we introduce a new insight and a potentially promising framework that could broaden the application of information integration across a wider spectrum. Furthermore, this new framework offers computational convenience compared to classic empirical likelihood-based methods. We provide numerical evaluations to assess its performance and discuss various extensions in the end.