We study the performance of machine learning techniques to the problem of identifying speakers at meetings from anonymous minutes issued afterwards. The data comes from board meetings of Sveriges Riksbank (Sweden’s Central Bank). The data is split in two ways, one where each reported contribution to the discussion is treated as a data point, and another where all contributions from a single speaker have been aggregated. Using interpretable models we find that lexical features and topic models generated from speeches held by the board members outside of board meetings are good predictors of speaker identity. Combining topic models with other features gives prediction accuracies close to 80% on aggregated data, though there is still a sizeable gap in performance compared to a not easily interpreted BERT-based transformer model that we offer as a benchmark.

We present results from a study investigating how users perceive text quality and readability in extractive and abstractive summaries. We trained two summarisation models on Swedish news data and used these to produce summaries of articles. With the produced summaries, we conducted an online survey in which the extractive summaries were compared to the abstractive summaries in terms of fluency, adequacy and simplicity. We found statistically significant differences in perceived fluency and adequacy between abstractive and extractive summaries but no statistically significant difference in simplicity. Extractive summaries were preferred in most cases, possibly due to the types of errors the summaries tend to have.