Federated learning enables collaborative modeling across institutions while preserving data privacy. However, achieving accurate and efficient feature alignment remains a significant challenge, particularly in scenarios with nonoverlapping feature distributions. To address this issue, we propose P-FedAlign, a privacy-enhanced federated feature alignment method based on secure multi-party computation (SMPC). Our approach leverages cryptographic protocols to perform feature matching among participants without exposing raw data, effectively mitigating potential privacy leakage risks. Furthermore, an efficient acceleration mechanism is integrated to reduce protocol overhead and enhance alignment efficiency. Experimental results demonstrate that P-FedAlign outperforms existing methods in terms of alignment accuracy, privacy protection, and computational performance, making it suitable for various federated learning applications.

Previous approaches for automatic lay summarisation are exclusively reliant on the source article that, given it is written for a technical audience (e.g., researchers), is unlikely to explicitly define all technical concepts or state all of the background information that is relevant for a lay audience.We address this issue by augmenting eLife, an existing biomedical lay summarisation dataset, with article-specific knowledge graphs, each containing detailed information on relevant biomedical concepts.Using both automatic and human evaluations, we systematically investigate the effectiveness of three different approaches for incorporating knowledge graphs within lay summarisation models, with each method targeting a distinct area of the encoder-decoder model architecture.Our results confirm that integrating graphbased domain knowledge can significantly benefit lay summarisation by substantially increasing the readability of generated text and improving the explanation of technical concepts.1

Existing unsupervised keyphrase extraction methods typically emphasize the importance of the candidate keyphrase itself, ignoring other important factors such as the influence of uninformative sentences. We hypothesize that the salient sentences of a document are particularly important as they are most likely to contain keyphrases, especially for long documents. To our knowledge, our work is the first attempt to exploit sentence salience for unsupervised keyphrase extraction by modeling hierarchical multi-granularity features. Specifically, we propose a novel position-aware graph-based unsupervised keyphrase extraction model, which includes two model variants. The pipeline model first extracts salient sentences from the document, followed by keyphrase extraction from the extracted salient sentences. In contrast to the pipeline model which models multi-granularity features in a two-stage paradigm, the joint model accounts for both sentence and phrase representations of the source document simultaneously via hierarchical graphs. Concretely, the sentence nodes are introduced as an inductive bias, injecting sentence-level information for determining the importance of candidate keyphrases. We compare our model against strong baselines on three benchmark datasets including Inspec, DUC 2001, and SemEval 2010. Experimental results show that the simple pipeline-based approach achieves promising results, indicating that keyphrase extraction task benefits from the salient sentence extraction task. The joint model, which mitigates the potential accumulated error of the pipeline model, gives the best performance and achieves new state-of-the-art results while generalizing better on data from different domains and with different lengths. In particular, for the SemEval 2010 dataset consisting of long documents, our joint model outperforms the strongest baseline UKERank by 3.48%, 3.69% and 4.84% in terms of [email protected], [email protected] and [email protected], respectively. We also conduct qualitative experiments to validate the effectiveness of our model components.

Lay summarisation aims to jointly summarise and simplify a given text, thus making its content more comprehensible to non-experts.Automatic approaches for lay summarisation can provide significant value in broadening access to scientific literature, enabling a greater degree of both interdisciplinary knowledge sharing and public understanding when it comes to research findings.However, current corpora for this task are limited in their size and scope, hindering the development of broadly applicable data-driven approaches.Aiming to rectify these issues, we present two novel lay summarisation datasets, PLOS (large-scale) and eLife (medium-scale), each of which contains biomedical journal articles alongside expert-written lay summaries.We provide a thorough characterisation of our lay summaries, highlighting differing levels of readability and abstractiveness between datasets that can be leveraged to support the needs of different applications.Finally, we benchmark our datasets using mainstream summarisation approaches and perform a manual evaluation with domain experts, demonstrating their utility and casting light on the key challenges of this task.Our code and datasets are available at https://github.com/TGoldsack1/ Corpora_for_Lay_Summarisation.

Aspect Sentiment Triplet Extraction (ASTE) is a relatively new and very challenging task that attempts to provide an integral solution for aspect-based sentiment analysis. Aspect sentiment triplets in a sentence usually have overlaps when, e.g., one aspect is associated with multiple opinions and vice versa. Recently, end-to-end ASTE methods are becoming more and more popular for they can avoid the error propagation problem of pipeline-based methods. However, existing tagging-based end-to-end methods face difficulty to obtain a satisfactory recall, and generative methods fail to take a full account of the underlying interactions between aspects, opinions and their corresponding sentiments. In this paper, we formalize the ASTE task as a Seq2Seq learning problem with span copy mechanism for extracting multiple and possibly overlapped triplets. A novel dual decoder is devised purposefully for the ASTE task, where a multi-head attention based span copy mechanism is proposed to copy multi-token aspects and opinions. The dual decoder benefits from the rich output of encoder that can fuse multi-type information including word semantic, POS tag and BIO tag. Experiments on various benchmark datasets demonstrate that our approach achieves new state-of-the-art results. We also conduct analytical experiments to verify the effectiveness of various model components particularly for overlapped triplets extraction. We find that our model can be further improved through data augmentation and post-training.