## 5 Discussion  
This section aims at illustrating the impact of our approach to domain-specific information extraction tasks, through an end-to-end example (Table 7) which showcases the added value of each individual component that comprises our pipeline compared to a standalone OIE process. For reasons of better readability, we have included only a small extract of the processed article body. As seen in the upper part of Table 7, the coreference resolution component correctly substitutes an entity mention (\"the bats\") to its more semantically informative antecedent (\"horseshoe bats\"). Subsequently, the extractive summarization component identifies a set of salient points and groups them to a much shorter, concise corpus, which is subjected to the parallel triple extraction process. The extracted triples are finally passed through an entity enrichment and cleaning process, that results in storing only those linked to one or more UMLS concepts. Other provided information including polarity, UMLS coded IDs, matching confidence scores, concept descriptions as well as the Neo4 graph visualization of the extracted triples are omitted in this example. For the sake of comparison, a small subset of the triples derived from the application of a standalone, state-of-the-art OIE engine (ClausIE) on the raw article body is provided at the lower part of Table 7. It is apparent from the triple samples that the lack of pre-processing (coreference resolution, summarization) and post-processing (entity resolution, cleaning) methods, usually leads to a large number of uninformative triples, despite the fact of some being syntactically correct. For example, the triple [\"This finding\", \"implies\", \"a possible recombination event\"] although being valid, it unpacks little contextual value with regard to the aims of the article. On the contrary, our approach seems to increase the expressiveness and informativeness of the derived triples, ensuring that they remain relevant to the context of the article. It should be noted that the inclusion of the aforementioned pre-processing and post-processing operations increases the computational cost of our pipeline compared to adopting a standalone OIE approach; however, this is partially countered by the fact that the core triple extraction process is applied to a significantly shorter and more concise corpus. Furthermore, it is evident that, although the results seem encouraging, none of the pipeline components are guaranteed to produce optimal results in every complex situation. More specifically, the extractive summarization component may fail to capture all the key points of the article resulting in loss of information, the in-place coreference resolution component may fail to find the correct antecedent of a mention, the parallel triple extraction process may miss some triples involving compound syntactic phenomena, and the entity enrichment tool may perform a wrong linking to a knowledge base concept. This is to be expected in real-world machine learning applications, where extractive summarization can be conceived as a feature selection method, in-place coreference resolution can be considered as a feature transformation technique, and entity linking can be seen as a data filtering method, each of them contributing to the usefulness of the overall approach. As discussed in Section 4, the added value of our methodology stems from the effective combination of different NLP tasks, benefiting from their distinct characteristics in an attempt to provide a robust outcome. Figure 6: A sample sequence of extracted triples from an _article’s_ (purple) sentences as alternating _subject_ (green) and _object_ (red) nodes, linked with _predicate_ (grey) and _followed_by_ (yellow) edges. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Estarated triples using our pipeline**} \\ \hline