Natural language reports generated by existing systems ignore the historical context of the facts and events they relate. In this paper, I argue that going beyond this limitation requires abandoning the pipelined architecture of existing report generators. I propose a new architecture in which a first draft of the report is organized around new information and then incrementally revised to opportunistically add related historical information. This type of information-adding revision allows to elaborate inside a clause or a nominal while taking into account surface structure constraints from any other portion of the report. In addition to providing the additional flexibility required to convey historical information, the proposed architecture constitutes an interesting testbed to investigate a wide range of open questions concerning content planning below the sentence level, generation with revision and generation architecture. 1 Introduction: generating reports in their historical co...

The complex sentences of newswire reports contain floating content units that appear to be opportunistically placed where the form of the surrounding text allows. We present a corpus analysis that identified precise semantic and syntactic constraints on where and how such information is realized. The result is a set of revision tools that form the rule base for a report generation system, allowing incremental generation of complex sentences. Introduction Generating reports that summarize quantitative data raises several challenges for language generation systems. First, sentences in such reports are very complex (e.g., in newswire basketball game summaries the lead sentence ranges from 21 to 46 words in length). Second, while some content units consistently appear in fixed locations across reports (e.g., game results are always conveyed in the lead sentence), others float, appearing anywhere in a report and at different linguistic ranks within a given sentence. Floating content uni...