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Recent advances in automatic text summarization have used deep neural networks to generate high-quality abstractive summaries, but the performance of these models strongly depends on large amounts of suitable training data. We propose a new method for mining social media for author-provided summaries, taking advantage of the common practice of appending a "TL;DR" to long posts. A case study using a large Reddit crawl yields the Webis-TLDR-17 corpus, complementing existing corpora primarily from the news genre. Our technique is likely applicable to other social media sites and general web crawls.

Peripheral nerve regeneration within guidance conduits involves a critical association between regenerating axons, Schwann cells (SCs), and neovascularization. However, it is currently unknown if there is a greater association between these factors in nonpermeable versus semipermeable nerve guide conduits. We therefore examined this collaboration in both silicone- and collagen-based nerve conduits in both 5- and 10-mm-injury gaps in rat sciatic nerves. Results indicate that collagen conduits promoted enhanced axonal and SC regeneration and association when compared to silicone conduits in the shorter 5-mm-gap model. In addition, collagen tubes displayed enhanced neovascularization over silicone conduits, suggesting that these three factors are intimately related in successful peripheral nerve regeneration. At later time points (1- and 2-month analysis) in a 10-mm-gap model, collagen tubes displayed enhanced axonal regeneration, myelination, and vascularization when compared to silicone-based conduits. Results from these studies suggest that regenerating cables within collagen-based conduits are revascularized earlier and more completely, which in turn enhances peripheral nerve regeneration through these nerve guides as compared to silicone conduits.

Given any argument on any controversial topic, how to counter it? This question implies the challenging retrieval task of finding the best counterargument. Since prior knowledge of a topic cannot be expected in general, we hypothesize the best counterargument to invoke the same aspects as the argument while having the opposite stance. To operationalize our hypothesis, we simultaneously model the similarity and dissimilarity of pairs of arguments, based on the words and embeddings of the arguments' premises and conclusions. A salient property of our model is its independence from the topic at hand, i.e., it applies to arbitrary arguments. We evaluate different model variations on millions of argument pairs derived from the web portal idebate.org. Systematic ranking experiments suggest that our hypothesis is true for many arguments: For 7.6 candidates with opposing stance on average, we rank the best counterargument highest with 60% accuracy. Even among all 2801 test set pairs as candidates, we still find the best one about every third time.