In order to reduce the confounding variables, we kept the ordering of content in all systems the same, by adopting the ordering of the rule-based system.

Rule-based System: generates summaries based on Content Selection rules derived by working with a L&T expert and a student (Gkatzia et al., 2013).

Development of time-series generation systems (Section 4.2, Section 5.3): ML system, RL system, Rule-based and Random system 5.

from left to right: ML system, RL, rule-based and randor.

\ P l R \ F Rule-based baseline 0.85 0.10 0.18 Supervised 0.62 0.28 0.39

For a baseline, we consider a rule-based model that simply learns all ngram segmentations seen in the training data, and marks any occurrence of a matching token sequence as a motif; without taking neighbouring context into account.

However, the rule-based method has a very row recall due to lack of generalization capabilities.

While rule-based approaches provide a natural way to express expert knowledge, it is relatively difficult to en-

general, many different rule-based systems, e.g.

However, rule-based approaches dominated in resolution; none of the top performers attempted to learn to do resolution.

3.1 Rule-Based Segmentation Algorithm

Algorithm 1 Rule-based segmentation.

For each sentiment, the Triple Extractor (TE) extracts candidate dependency relation triples using a novel rule-based approach.