Textual Summarisation of Large Sets: Towards a General Approach
This work addresses the need for automated summarization of sets in domains like academic research and e-commerce, but it is incremental as it builds on prior work with a focus on generalization.
The paper tackles the problem of generating textual summaries for large sets of objects, presenting a rule-based NLG technique evaluated on bibliographical references in academic papers, which extends previous work on consumer products and demonstrates generalization across these two domains.
We are developing techniques to generate summary descriptions of sets of objects. In this paper, we present and evaluate a rule-based NLG technique for summarising sets of bibliographical references in academic papers. This extends our previous work on summarising sets of consumer products and shows how our model generalises across these two very different domains.