From these paragraphs, three key parameters were extracted: a sequence of synthesis actions taken, a table of constituent chemicals, and the reaction conditions (i.e. temperatures and quoted times). For each parameter, the F1-score was calculated providing a numeric score for each text mining task compared against the manually-extracted ground truth. Extracted chemical identities were cross-referenced against the PubChem database of compounds to act as both a unique identifier and source of key information about each species. Finally, physical quantities -the values of time, temperature, and chemical quantity -were converted from plain text to numerical units using the pint python library and compared against their manually extracted counterparts. These data are summarised in Table . Individual synthesis actions were relatively poorly identified with text mining, with a F1-score of ca. 60%. This was primarily due to the low recall (i.e. true positive) rate of action parsing. Inspection of the synthesis paragraphs themselves showed that actions that were implicitly repeated, for example in the phrase "washed with water and methanol subsequently for 3 times", were not captured by ChemicalTagger thereby leading to lower scores. Conversely, when synthesis actions were converted to their conceptual types and aggregated, the F1-score increased significantly to over 90% indicating that all synthesis stages were identified even if the specific ActionPhrases themselves were not. Therefore, we conclude that the text mining captures the essence of the synthesis protocol, but is unable to fully summarise the semantics of synthesis due to "linguistic noise" i.e. variability between different authors writing styles.