Sustainable text summarization over mobile devices: An energy-aware approach

Due to the omnipresence of hand-held mobile devices, people nowadays are running many applications in such devices to fulfill their daily life requirements. However, due to the limited energy (battery power) of mobile hand-held devices, the energy consumption of such applications determines their feasibility of running in such mobile devices for a long term basis. One such important application is the summarization of text information. Although almost all the existing summarization approaches to date are designed to run on high-end servers or cloud platforms aiming to optimize only the summary quality, there are many applications nowadays, e.g., summarizing data in crisis scenarios or summarizing privacy-sensitive data which demands the functionality of on-device computationally light-weight text summarization to generate effective summaries, while keeping in mind the limited battery power of the device. This paper is the first of its kind where we design energy-efficient summarization algorithms for mobile devices. First, we provide a methodology to systematically measure the energy consumption characteristics of various classical extractive summarization techniques at a modular level by analyzing the algorithmic constructs. Through this process, energy-hungry modules are identified under different configurations and environmental parameters, like input data type, dataset size, device type, among others. Next, based on the observations, we develop four classes of energy-efficient hybrid summarization approaches. Extensive experiments show that the hybrid summarization approaches, when applied on various datasets of varying size and type, can save up to 90% energy, with 5-40% degradation in the summary quality with respect to the high-performing base summarization approaches.