A Narrative Sentence Planner and Structurer for Domain Independent, Parameterizable Storytelling

Storytelling is an integral part of daily life and a key part of how we share information and connect with others. The ability to use Natural Language Generation (NLG) to produce stories that are tailored and adapted to the individual reader could have large impact in many different applications. However, one reason that this has not become a reality to date is the NLG story gap, a disconnect between the plan-type representations that story generation engines produce, and the linguistic representations needed by NLG engines. Here we describe Fabula Tales, a storytelling system supporting both story generation and NLG. With manual annotation of texts from existing stories using an intuitive user interface, Fabula Tales automatically extracts the underlying story representation and its accompanying syntactically grounded representation. Narratological and sentence planning parameters are applied to these structures to generate different versions of the story. We show how our storytelling system can alter the story at the sentence level, as well as the discourse level. We also show that our approach can be applied to different kinds of stories by testing our approach on both Aesop’s Fables and first-person blogs posted on social media. The content and genre of such stories varies widely, supporting our claim that our approach is general and domain independent. We then conduct several user studies to evaluate the generated story variations and show that Fabula Tales’ automatically produced variations are perceived as more immediate, interesting, and correct, and are preferred to a baseline generation system that does not use narrative parameters

A lifelogging system supporting multimodal access

Today, technology has progressed to allow us to capture our lives digitally such as taking pictures, recording videos and gaining access to WiFi to share experiences using smartphones. People’s lifestyles are changing. One example is from the traditional memo writing to the digital lifelog. Lifelogging is the process of using digital tools to collect personal data in order to illustrate the user’s daily life (Smith et al., 2011). The availability of smartphones embedded with different sensors such as camera and GPS has encouraged the development of lifelogging. It also has brought new challenges in multi-sensor data collection, large volume data storage, data analysis and appropriate representation of lifelog data across different devices. This study is designed to address the above challenges. A lifelogging system was developed to collect, store, analyse, and display multiple sensors’ data, i.e. supporting multimodal access. In this system, the multi-sensor data (also called data streams) is firstly transmitted from smartphone to server only when the phone is being charged. On the server side, six contexts are detected namely personal, time, location, social, activity and environment. Events are then segmented and a related narrative is generated. Finally, lifelog data is presented differently on three widely used devices which are the computer, smartphone and E-book reader. Lifelogging is likely to become a well-accepted technology in the coming years. Manual logging is not possible for most people and is not feasible in the long-term. Automatic lifelogging is needed. This study presents a lifelogging system which can automatically collect multi-sensor data, detect contexts, segment events, generate meaningful narratives and display the appropriate data on different devices based on their unique characteristics. The work in this thesis therefore contributes to automatic lifelogging development and in doing so makes a valuable contribution to the development of the field