This paper describes work to enhance a sentence-based summarizer with notions of salience, dynamically-adjustable summary size, discourse segmentation, and awareness of topic shifts. Our experiments study strategies to diversify the application of a baseline summarizer, by making it aware of finer-grained 'aboutness', capable of discerning changes of topic, and sensitive to longer-than-usual documents. Evaluated against the corpus used in the development of the baseline summarizer, summaries derived either by means of segmentation analysis alone, or by a mix of strategies for combining salience calculation and topic shift detection, are shown to be of comparable, and under certain conditions even better, quality. We describe the summarization and segmentation procedures, outline a number of strategies for mixing the two, evaluate the overall impact of discourse segmentation, and suggest an interface design capable of using the notion of topic shifts to contextualize a summary and facilitate the mediation between it and the full document source.