Detecting Emotive Quotes in Oral Testimonies

The engagement with archives related to narratives of personal trauma can be intimidating for non-experts due to the unsettling subject matter and the lack of clear starting points. One way to incentivize potential readers to learn more about victims' and survivors' stories is to quote, which has a long tradition of piquing interest in philosophy, literature, news, and pop culture. While these media are crafted for memorability, in this paper, we investigate whether this "quoting language" also emerges in unscripted speech and can be automatically extracted to enrich the presentation of archives and make the interaction with them more memorable. We train a sequence-to-sequence model for quote detection on available annotated corpora and evaluate how well its knowledge transfers to oral testimonies of Holocaust survivors to identify phrases that stand out even compared with the rest of these important stories. We manually annotate hundreds of automatically extracted text excerpts and assess their characteristics to identify what constitutes a meaningful quote in this domain, and show that this can be decided computationally.