Multi-Target Cross-Lingual Summarization: a novel task and a language-neutral approach (2024)

Abstract: Cross-lingual summarization aims to bridge language barriers by summarizing documents in different languages. However, ensuring semantic coherence across languages is an overlooked challenge and can be critical in several contexts. To fill this gap, we introduce multi-target cross-lingual summarization as the task of summarizing a document into multiple target languages while ensuring that the produced summaries are semantically similar. We propose a principled re-ranking approach to this problem and a multi-criteria evaluation protocol to assess semantic coherence across target languages, marking a first step that will hopefully stimulate further research on this problem.

## introduction cross-lingual summarization refers to the task of producing a summary in a different language than the original document and has the potential to break language barriers by helping people to effectively capture the essence of documents written in foreign languages @xcite . this is a very challenging task, as it combines the difficulties of monolingual summarization, such as factual inconsistencies with respect to the source document @xcite , with those of machine translation, such as translation of idiomatic expressions and cultural references @xcite . the availability of large pre-trained multilingual transformers @xcite , followed by the widespread development and adoption of decoder-only language models @xcite @xcite has enabled a single model to perform cross-lingual summarization from multiple source languages to multiple target languages (many-to-many summarization, m2ms). despite the increasing emphasis on this many-tomany paradigm, ensuring semantic coherence in summaries across different target languages has not been a primary focus of state-of-the-art methods, nor has it been systematically evaluated. to fill this gap, we introduce a novel variant of cross-lingual summarization, which we call multitarget cross-lingual summarization (mtxls), where we specifically address the challenge of promoting semantic coherence across target languages. this framework represents an important step towards more comprehensive cross-lingual summarization techniques and evaluation. our main contributions in this work are summarized as follows: first, we introduce mtxls formally as a novel task (section 3), motivated by the need of producing summaries coherently for multiple target languages. second, we present a re-ranking-based approach to address this problem (section 4), where the re-ranking phase selects a set of summaries that exhibit superior semantic coherence across target languages compared to treating each cross-lingual summarization task independently. notably, our approach circumvents the need for a pivot language. this language-neutral strategy ensures a more robust and unbiased multilingual summarization process. finally, we propose and conduct a multicriteria evaluation protocol that goes beyond the simple evaluation of the similarity between gener-text (bbc) ## conclusion this work introduces multi-target cross-lingual summarization to address the challenge of achieving coherent summaries across multiple target languages. we propose two re-ranking approaches tailored to this task, which improve semantic coherence across languages compared to conventional beam search decoding, while still preserving similarity to the reference summaries. in particular, one of these methods eliminates the need for a pivot language, thus treating all languages equally and eliminating potential biases arising from pivot language selection. furthermore, we extended the evaluation framework for cross-lingual summarization by including the assessment of semantic coherence across different target languages.