Patients exposed to a surgical safety checklist experience better postoperative outcomes, but this could simply reflect wider quality of care in hospitals where checklist use is routine.

Due to the lack of publicly available resources, conversation summarization has received far less attention than text summarization. As the purpose of conversations is to exchange information between at least two interlocutors, key information about a certain topic is often scattered and spanned across multiple utterances and turns from different speakers. This phenomenon is more pronounced during spoken conversations, where speech characteristics such as backchanneling and false-starts might interrupt the topical flow. Moreover, topic diffusion and (intra-utterance) topic drift are also more common in human-to-human conversations. Such linguistic characteristics of dialogue topics make sentence-level extractive summarization approaches used in spoken documents illsuited for summarizing conversations. Pointer-generator networks have effectively demonstrated its strength at integrating extractive and abstractive capabilities through neural modeling in text summarization. To the best of our knowledge, to date no one has adopted it for summarizing conversations. In this work, we propose a topic-aware architecture to exploit the inherent hierarchical structure in conversations to further adapt the pointer-generator model. Our approach significantly outperforms competitive baselines, achieves more efficient learning outcomes, and attains more robust performance.

Weak adhesion and lack of underwater self‐healability hinder advancing soft iontronics particularly in wet environments like sweaty skin and biological fluids. Mussel‐inspired, liquid‐free ionoelastomers are reported based on seminal thermal ring‐opening polymerization of a biomass molecule of α‐lipoic acid (LA), followed by sequentially incorporating dopamine methacrylamide as a chain extender, N,N′‐bis(acryloyl) cystamine, and lithium bis(trifluoromethanesulphonyl) imide (LiTFSI). The ionoelastomers exhibit universal adhesion to 12 substrates in both dry and wet states, superfast self‐healing underwater, sensing capability for monitoring human motion, and flame retardancy. The underwater self‐repairabilitiy prolongs over three months without deterioration, and sustains even when mechanical properties greatly increase. The unprecedented underwater self‐mendability benefits synergistically from the maximized availability of dynamic disulfide bonds and diverse reversible noncovalent interactions endowed by carboxylic groups, catechols, and LiTFSI, along with the prevented depolymerization by LiTFSI and tunability in mechanical strength. The ionic conductivity reaches 1.4 × 10−6–2.7 × 10−5 S m−1 because of partial dissociation of LiTFSI. The design rationale offers a new route for creating a wide range of LA‐ and sulfur‐derived supramolecular (bio)polymers with superior adhesion, healability, and other functionalities, and thus has technological implications for coatings, adhesives, binders and sealants, biomedical engineering and drug delivery, wearable and flexible electronics, and human–machine interfaces.

Extractive summarization selects and concatenates the most essential text spans in a document. Most, if not all, neural approaches use sentences as the elementary unit to select content for summarization. However, semantic segments containing supplementary information or descriptive details are often nonessential in the generated summaries. In this work, we propose to exploit discourse-level segmentation as a finer-grained means to more precisely pinpoint the core content in a document. We investigate how the sub-sentential segmentation improves extractive summarization performance when content selection is modeled through two basic neural network architectures and a deep bi-directional transformer. Experiment results on the CNN/Daily Mail dataset show that discourse-level segmentation is effective in both cases. In particular, we achieve state-of-the-art performance when discourse-level segmentation is combined with our adapted contextual representation model.