Filter and export

Publications

What is a Publication?
1539 Publications visible to you, out of a total of 1539
Cross-lingual Science Journalism: Select, Simplify and Rewrite Summaries for Non-expert Readers

Abstract (Expand)

Automating Cross-lingual Science Journalism (CSJ) aims to generate popular science summaries from English scientific texts for non-expert readers in their local language. We introduce CSJ as a downstream … task of text simplification and cross-lingual scientific summarization to facilitate science journalists’ work. We analyze the performance of possible existing solutions as baselines for the CSJ task. Based on these findings, we propose to combine the three components - SELECT, SIMPLIFY and REWRITE (SSR) to produce cross-lingual simplified science summaries for non-expert readers. Our empirical evaluation on the WIKIPEDIA dataset shows that SSR significantly outperforms the baselines for the CSJ task and can serve as a strong baseline for future work. We also perform an ablation study investigating the impact of individual components of SSR. Further, we analyze the performance of SSR on a high-quality, real-world CSJ dataset with human evaluation and in-depth analysis, demonstrating the superior performance of SSR for CSJ.

Authors: Mehwish Fatima, Michael Strube

Date Published: 8th Jul 2023

Publication Type: InProceedings

Modeling Structural Similarities between Documents for Coherence Assessment with Graph Convolutional Networks

Abstract (Expand)

Coherence is an important aspect of text quality, and various approaches have been applied to coherence modeling. However, existing methods solely focus on a single document’s coherence patterns, … ignoring the underlying correlation between documents. We investigate a GCN-based coherence model that is capable of capturing structural similarities between documents. Our model first identifies the graph structure of each document, from where we mine different sub-graph patterns. We then construct a heterogeneous graph for the training corpus, connecting documents based on their shared subgraphs. Finally, a GCN is applied to the heterogeneous graph to model the connectivity relationships. We evaluate our method on two tasks, assessing discourse coherence and automated essay scoring. Results show that our GCN-based model outperforms baselines, achieving a new state-of-the-art on both tasks.

Authors: Wei Liu, Xiyan Fu, Michael Strube

Date Published: 8th Jul 2023

Publication Type: InProceedings

Annotation-Inspired Implicit Discourse Relation Classification with Auxiliary Discourse Connective Generation

Abstract (Expand)

Implicit discourse relation classification is a challenging task due to the absence of discourse connectives. To overcome this issue, we design an end-to-end neural model to explicitly generate discourse … connectives for the task, inspired by the annotation process of PDTB. Specifically, our model jointly learns to generate discourse connectives between arguments and predict discourse relations based on the arguments and the generated connectives. To prevent our relation classifier from being misled by poor connectives generated at the early stage of training while alleviating the discrepancy between training and inference, we adopt Scheduled Sampling to the joint learning. We evaluate our method on three benchmarks, PDTB 2.0, PDTB 3.0, and PCC. Results show that our joint model significantly outperforms various baselines on three datasets, demonstrating its superiority for the task.

Authors: Wei Liu, Michael Strube

Date Published: 8th Jul 2023

Publication Type: InProceedings

Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States

Abstract

Not specified

Authors: Evan L. Ray, Logan C. Brooks, Jacob Bien, Matthew Biggerstaff, Nikos I. Bosse, Johannes Bracher, Estee Y. Cramer, Sebastian Funk, Aaron Gerding, Michael A. Johansson, Aaron Rumack, Yijin Wang, Martha Zorn, Ryan J. Tibshirani, Nicholas G. Reich

Date Published: 1st Jul 2023

Publication Type: Journal

X-Shooting ULLYSES: Massive stars at low metallicity. I. Project description

Abstract (Expand)

Observations of individual massive stars, super-luminous supernovae, gamma-ray bursts, and gravitational wave events involving spectacular black hole mergers indicate that the low-metallicity Universe … is fundamentally different from our own Galaxy. Many transient phenomena will remain enigmatic until we achieve a firm understanding of the physics and evolution of massive stars at low metallicity (Z). The Hubble Space Telescope has devoted 500 orbits to observing ∼250 massive stars at low Z in the ultraviolet (UV) with the COS and STIS spectrographs under the ULLYSES programme. The complementary X-Shooting ULLYSES (XShootU) project provides an enhanced legacy value with high-quality optical and near-infrared spectra obtained with the wide-wavelength coverage X-shooter spectrograph at ESO’s Very Large Telescope. We present an overview of the XShootU project, showing that combining ULLYSES UV and XShootU optical spectra is critical for the uniform determination of stellar parameters such as effective temperature, surface gravity, luminosity, and abundances, as well as wind properties such as mass-loss rates as a function of Z. As uncertainties in stellar and wind parameters percolate into many adjacent areas of astrophysics, the data and modelling of the XShootU project is expected to be a game changer for our physical understanding of massive stars at low Z. To be able to confidently interpret James Webb Space Telescope spectra of the first stellar generations, the individual spectra of low-Z stars need to be understood, which is exactly where XShootU can deliver. Table B.1 and full Table B.2 are available at the CDS via anonymous ftp to cdsarc.cds.unistra.fr (ftp://130.79.128.5) or via https://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/675/A154 Based on observations collected at the European Southern Observatory under ESO programme 106.211Z.001.

Authors: Jorick S. Vink, A. Mehner, P. A. Crowther, A. Fullerton, M. Garcia, F. Martins, N. Morrell, L. M. Oskinova, N. St-Louis, A. ud-Doula, A. A. C. Sander, H. Sana, J. -C. Bouret, B. Kubátová, P. Marchant, L. P. Martins, A. Wofford, J. Th. van Loon, O. Grace Telford, Y. Götberg, D. M. Bowman, C. Erba, V. M. Kalari, M. Abdul-Masih, T. Alkousa, F. Backs, C. L. Barbosa, S. R. Berlanas, M. Bernini-Peron, J. M. Bestenlehner, R. Blomme, J. Bodensteiner, S. A. Brands, C. J. Evans, A. David-Uraz, F. A. Driessen, K. Dsilva, S. Geen, V. M. A. Gómez-González, L. Grassitelli, W. -R. Hamann, C. Hawcroft, A. Herrero, E. R. Higgins, D. John Hillier, R. Ignace, A. G. Istrate, L. Kaper, N. D. Kee, C. Kehrig, Z. Keszthelyi, J. Klencki, A. de Koter, R. Kuiper, E. Laplace, C. J. K. Larkin, R. R. Lefever, C. Leitherer, D. J. Lennon, L. Mahy, J. Maíz Apellániz, G. Maravelias, W. Marcolino, A. F. McLeod, S. E. de Mink, F. Najarro, M. S. Oey, T. N. Parsons, D. Pauli, M. G. Pedersen, R. K. Prinja, V. Ramachandran, M. C. Ramírez-Tannus, G. N. Sabhahit, A. Schootemeijer, S. Reyero Serantes, T. Shenar, G. S. Stringfellow, N. Sudnik, F. Tramper, L. Wang

Date Published: 1st Jul 2023

Publication Type: Journal

Bimodal Black Hole Mass Distribution and Chirp Masses of Binary Black Hole Mergers

Abstract

Not specified

Authors: Fabian R. N. Schneider, Philipp Podsiadlowski, Eva Laplace

Date Published: 15th Jun 2023

Publication Type: Journal

ISO/TS 9491-1:2023 Biotechnology — Predictive computational models in personalized medicine research — Part 1: Constructing, verifying and validating models

Abstract (Expand)

This document defines challenges and requirements for predictive computational models constructed for research purposes in personalized medicine. It specifies recommendations and requirements for the … setup, formatting, validation, simulation, storing and sharing of such models, as well as their application in clinical trials and other research areas. It summarizes specific challenges regarding data input, as well as verifying and validating of such models that can be considered as best practices for modelling in research and development in the field of personalized medicine. This document also specifies recommendations and requirements for data used to construct or needed for validating models, including rules and requirements for formatting, description, annotation, interoperability, integration, accessing, as well as recording and documenting the provenance of such data. This document does not provide specific rules or requirements for the use of computational models in the clinical routine, or for diagnostic or therapeutic purposes.

Authors: Marc Kirschner, Martin Golebiewski, Heike Moser, EU-STANDS4PM consortium, ISO/TC 276/WG 5

Date Published: 8th Jun 2023

Publication Type: Manual

Powered by
 (v.1.14.2)
About HITS Publication SEEK | About HITS-Publication-SEEK | Funding and Programmes | Credits
Copyright © 2008 - 2023 The University of Manchester and HITS gGmbH