The automatic synthesis of biomedical publications catalyzes a profound research interest elicited by literature congestion. Current sequence-to-sequence models mainly rely on the lexical surface and seldom consider the deep semantic interconnections between the entities mentioned in the source document. Such superficiality translates into fabricated, poorly informative, redundant, and near-extractive summaries that severely restrict their real-world application in biomedicine, where the specialized jargon and the convoluted facts further emphasize task complexity. To fill this gap, we argue that the summarizer should acquire semantic interpretation over input, exploiting structured and unambiguous representations to capture and conserve the most relevant parts of the text content. This paper presents CogitoErgoSumm, the first framework for biomedical abstractive summarization equipping large pre-trained language models with rich semantic graphs. Precisely, we infuse graphs from two complementary semantic parsing techniques with different goals and granularities—Event Extraction and Abstract Meaning Representation, also designing a reward signal to maximize information content preservation through reinforcement learning. Extensive quantitative and qualitative evaluations on the CDSR dataset show that our solution achieves competitive performance according to multiple metrics, despite using 2.5x fewer parameters. Results and ablation studies indicate that our joint text-graph model generates more enlightening, readable, and consistent summaries. Code available at: https://github.com/disi-unibo-nlp/cogito-ergo-summ.

In this paper, we address the problem of multi-modal retrieval of fashion products. State-of-the-art (SOTA) works proposed in literature use vision-and-language transformers to assign similarity scores to joint text-image pairs, then used for sorting the results during a retrieval phase. However, this approach is inefficient since it requires coupling a query with every record in the dataset and computing a forward pass for each sample at runtime, precluding scalability to large-scale datasets. We thus propose a solution that overcomes the above limitation by combining transformers and deep metric learning to create a latent space where texts and images are separately embedded, and their spatial proximity translates into semantic similarity. Our architecture does not use convolutional neural networks to process images, allowing us to test different levels of image-processing details and metric learning losses. We vastly improve retrieval accuracy results on the FashionGen benchmark (+18.71% and +9.22% [email protected] on Image-to-Text and Text-to-Image, respectively) while being up to 512x faster. Finally, we analyze the speed-up obtainable by different approximate nearest neighbor retrieval strategies—an optimization precluded to current SOTA contributions. We release our solution as a web application available at https://disi-unibo-nlp.github.io/projects/fashion_retrieval/.

Despite the large amount of available exoskeletons, their use in daily life is still limited due to the absence of testing in real-life environments. Thus, the present work aims to test on a series of uneven terrains a wearable ankle exoskeleton, named RANK, designed for walking assistance and drop-foot prevention. RANK consists of a 3D-printed brace attached to the user and a piezoresistive insole, to be incorporated into the user's shoe. Real-time analysis of the insole's sensor outputs enables the control system to provide torque assistance to the ankle joint through a four-bar linkage mechanism. Two healthy male subjects were enrollee, asking them to walk on three different terrain conditions (flat, soft, and irregular) with and without exoskeleton. Human kinematics was gathered via inertial measurements units (IMUs). The effects of ankle exoskeleton on lower limb joint angles were assessed in terms of range of motion (ROM), whereas statistical parametric map method was applied to compare joint angle curves. As expected, a reduction of the ankle ROM approximatively of 10° was found in all terrain conditions between the trails performed with and without exoskeleton. No effects induced on the hip and knee joint were observed. Moreover, no significant differences were found over the almost totality of the gait cycle regardless the terrain conditions. Results demonstrate the capability of the exoskeleton to work properly regardless the type of walking surface.