Narrative generation by associative network extraction from real-life temporal data

Data about events abounds in our technological society. An attractive way of presenting real-life temporal data to facilitate its interpretation is an automatically generated narrative. Narrative comprehension involves the construction of a causal network by the reader. Narrative data-to-text systems seem to acknowledge causal relations as important. However, they play a secondary role in their document planners and their identification relies mostly on domain knowledge.  This thesis proposes an assisted temporal data interpretation model by narrative generation in which narratives are structured with the help of a mix of automatically mined and manually defined association rules. The associations suggest causal hypotheses to the reader who can thus construct more easily a causal representation of the events. This model should be applicable to any repetitive temporal data, preferably including actions or activities, such as Activity of Daily Living (ADL) data.  Sequential association rules are selected based on the criteria of confidence and statistical significance as measured in training data. World and domain knowledge association rules are based on the similarity of some aspect of a pair of events or on causal patterns difficult to detect statistically.  To interpret a specific period to summarize, pairs of events for which an association rule applies are associated. Some extra associations are then derived. Together the events and associations form an associative network.  The most important step of the Natural Language Generation (NLG) pipeline is document planning, comprising event selection and document structuring. For event selection, the model relies on the confidence of sequential associations to select the most unusual facts. The assumption is that an event that is implied by another one with a relatively high probability may be left implicit in the text.  The structure of the narrative is called the connecting associative thread because it allows the reader to follow associations from the beginning to the end of the text. It takes the form of a spanning tree over the previously selected associative sub-network. The associations it contains are selected based on association type preferences and relative temporal distance. The connecting associative thread is then segmented into paragraphs, sentences, and phrases and the associations are translated to rhetorical relations.  The microplanning step defines lexico-syntactic templates describing each event type. When two event descriptions need to be assembled in the same sentence, a discourse marker expressing the specified rhetorical relation is employed. A main event and a preceding main event are determined for each sentence. When the associative thread parent of the main event is not the preceding main event, an anaphor is added to the sentence front discourse marker.  Surface realization can be performed in English or French thanks to bilingual lexico-syntactic specifications and the SimpleNLG-EnFr Java library.  The results of a textual quality evaluation show that the texts are understandable and the lexical choices adequate.

Keyphrases: Data-to-text, Relation causale, Natural language generation, Document planning, Narrative, Temporal data, Causal relation, Association rule, Sequence, Event, Domain knowledge