How to Train Your AI PA: A Novel Approach to Timeline Evaluation and Inference
We live in a time where information is more readily available in greater quantities than ever before. The question is how do me make the best use of it? One recent method for summarising massive amounts of data and presenting it in an accessible way is timeline generation.
Timeline generation (TLG) is a way of representing a large amount of temporally dependent information concisely. It is query driven; we retrieve a corpus of text linked to some entity, event or other term. We then select a number of the constituent sentences, timestamp them and return them as output (Fig 1). The canonical TLG model makes this selection by fitting a topic model over the corpus. This is used to cluster these articles into stories. The most relevant of these stories are selected and summarised through some flavour of sentence-selection. It can be seen as a generalisation of the multi-document summarisation task, where we have introduced temporal dependency and structure.
In this paper, we first outline the canonical timeline generation model. We look at several domains where it has been applied, as well as its statistical foundation. Through surveying the current body of work for this model, we define two fundamental issues with current implementations: namely the process by which timelines are evaluated and how inference is performed. Our paper offers novel and rigorous solutions to both of these issues. We provide an innovative and scientifically-rigourous framework for evaluating the quality of a timeline. We also develop a novel method for performing inference on the timeline models. We use the former to evaluate the latter, and present our full methodological design and results. Finally we include two appendices., The first outlines the development and evaluation of our baseline model, which is an intergral part of the entire evaluative process but nevertheless distinct from the rest of the paper. The second covers the ROUGE metric in more detail.