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Summarizing texts is not a straightforward task. Before even considering textsummarization, one should determine what kind of summary is expected. How muchshould the information be compressed? Is it relevant to reformulate or shouldthe summary stick to the original phrasing? State-of-the-art on automatic textsummarization mostly revolves around news articles. We suggest that consideringa wider variety of tasks would lead to an improvement in the field, in terms ofgeneralization and robustness. We explore meeting summarization: generatingreports from automatic transcriptions. Our work consists in segmenting andaligning transcriptions with respect to reports, to get a suitable dataset forneural summarization. Using a bootstrapping approach, we provide pre-alignmentsthat are corrected by human annotators, making a validation set against whichwe evaluate automatic models. This consistently reduces annotators' efforts byproviding iteratively better pre-alignment and maximizes the corpus size byusing annotations from our automatic alignment models. Evaluation is conductedon \publicmeetings, a novel corpus of aligned public meetings. We reportautomatic alignment and summarization performances on this corpus and show thatautomatic alignment is relevant for data annotation since it leads to largeimprovement of almost +4 on all ROUGE scores on the summarization task.

Align then Summarize: Automatic Alignment Methods for Summarization Corpus Creation