Distributed Training: Train BART/T5 for Summarization using š¤ Transformers and Amazon SageMaker
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arXiv:2608. 19200v1 Announce Type: cross Abstract: Text summarization refers to the task of condensing a document into a shorter version while preserving its key information.
The PSK submission to the WMT 2026 Multilingual Instruction Shared Task employs a 3.35Bāparameter Tiny Aya Global model enhanced with three QLoRA adapters, each dedicated to a specific task: multilingual summarization, passageābased question answering, and filtered standalone question answering. The summarization adapter is trained on multilingual documentāsummary pairs, including scientific papers with authorāwritten abstracts, and outperforms a multitask adapter trained solely on organizer data on a heldāout split. For open question answering, results vary with answer length and evaluation method, prompting the submission of three systems that share the same context and summarization adapters but differ in their openāQA adapters.
Weāve applied reinforcement learning from human feedback to train language models that are better at summarization.
arXiv:2606. 19591v1 Announce Type: cross Abstract: In this technical report, we focus on solving the challenge of Vietnamese multi-document abstractive summarization, introduced in the International Workshop on Vietnamese Language and Speech Processing (VLSP) 2022.
arXiv:2606. 03867v1 Announce Type: cross Abstract: Multi-Document Summarization (MDS) plays a critical role in distilling essential information from collections of textual data.
arXiv:2608. 04307v1 Announce Type: cross Abstract: Text summarization is deceptively difficult.