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arXiv Machine Learning By Sujay Uday Rittikar, Sheela Ramanna

LexLattice: Multilingual Extractive Summarization via Neural Cellular Automata on Document Hierarchies

Read the original on arXiv Machine Learning →

LexLattice is an extractive summarizer that models a legal act’s hierarchy as a two‑dimensional semantic lattice and consolidates information over it using a masked 2D neural cellular automata before selecting content. The method achieves state‑of‑the‑art ROUGE scores across all 24 languages of EUR‑Lex‑Sum in both multilingual and cross‑lingual settings, outperforming large instruction‑tuned baselines while using only a 1.8 M‑parameter consolidator on a frozen multilingual encoder. A consolidator trained on high‑resource languages transfers almost losslessly to unseen languages, suggesting the model operates on language‑agnostic semantic geometry rather than surface form.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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