Towards Unifying Multi-Lingual and Cross-Lingual Summarization
Year: 2023

Abstract:
To adapt text summarization to the multilingual world, previous work proposes multi-lingual summarization (MLS) and cross-lingual summarization (CLS). However, these two tasks have been studied separately due to the different definitions, which limits the compatible and systematic research on both of them. In this paper, we aim to unify MLS and CLS into a more general setting, i.e., many-to-many summarization (M2MS), where a single model could process documents in any language and generate their summaries also in any language. As the first step towards M2MS, we conduct preliminary studies to show that M2MS can better transfer task knowledge across different languages than MLS and CLS. Furthermore, we propose PISCES, a pre-trained M2MS model that learns language modeling, cross-lingual ability and summarization ability via threestage pre-training. Experimental results indicate that our PISCES significantly outperforms the state-of-the-art baselines, especially in the zero-shot directions, where there is no training data from the source-language documents to the target-language summaries.1

heading: Future Work
text: We reserve this challenge for future work. Ablations. We conduct ablation studies to investigate the effect of the cross-lingual and task-specific pre-training stages. We run the following ablations: • PISCES w/o TS. To demonstrate the effective-
ness of the task-specific pre-training, we also pre-train a variant PISCES model which does not include the task-specific pre-training stage. • PISCES w/o CL. To measure the effectiveness of the cross-lingual pre-training, we remove this stage in the whole pre-training process, resulting in another variant PISCES. • PISCES w/o TS & CL removes both the crosslingual and task-specific pre-training stages,
13Results on Hi/Zh/Th⇒Tr are given in Appendix D 14Though PISCES has been pre-trained with pseudo M2MS samples, there is still a large gap between the pseudo samples and downstream samples, e. g. , text style and domain. How to Download Photos from Your iPhone to a Computer
Turkish Document iPhone’un şarj kablosunun bir ucunu iPhone’un şarj girişine tak, ardından USB ucunu bilgisayarının USB girişlerinden birine tak. Kilidini açmak için parolanı (veya TouchID’ni ya da FaceID’ni) gir ve iPhone’undaki Home düğmesine bas. Devam etmeden önce, istenirse "Bu bilgisayara güvenilsin mi?" kısmında Güven seçeneğine dokun. Mac’in Dock’unda çok renkli bir çarkıfeleğe benzeyen Fotoğraflar uygulaması simgesine tıkla. Fotoğraflar uygulaması iPhone’unu bağladığında otomatik olarak açılabilir. iPhone’un simgesi, uygulamanın penceresinin sol üst köşesinde görünmelidir. Fotoğrafların alınıp içeri aktarılacağı yer olarak pencerenin sol tarafında iPhone’unun adına tıkla. Bunu penceredeki resimlere tıklayarak yap. Bilgisayarında olmayan tüm fotoğrafları içeri aktarmak istiyorsan bu adımı atla. Bu, pencerenin sağ üst köşesindedir. Seçtiğin fotoğraf sayısı bu butonda görünecektir (örneğin, 5 Seçileni İçeri Aktar). iPhone’undaki Mac bilgisayarında olmayan tüm fotoğrafları aktarmak istiyorsan Tüm Yeni Ögeleri İçeri Aktar seçeneğine tıkla. Bu, pencerenin sol tarafındadır. Az önce aktardığın fotoğraflar bu sayfada listelenir. mBART Examine the iphone’s keyboard. Click the "screen" button to view the photos. Click the "screen" button to view the list of available photos. PISCES Connect your iphone to computer. Unlock your iphone. Click the "photos" app. Select the photos you wish to download. Click the "choose photos" option. Select the photos you wish to download. Click the "download" button. which is the same as mBART-50. As shown in Table 5, we conduct ablation studies in several conventional zero-shot directions (results in more directions are provided in Appendix E). In each case, the RS and BS are lower than vanilla PISCES. In addition, both PISCES w/o TS and PISCES w/o CL outperform PISCES w/o TS & CL. Thus, the effectiveness of both stages is proved. 5. 4 Qualitative Results
Human Evaluation. Following Zhu et al. (2020); Liang et al. (2022b), we conduct the human evaluation on 50 random samples extracted from WikiLingua (En⇒Zh, Zh⇒En and En⇒En, respectively). Three graduate students are invited to assess the generated summaries from three aspects: informativeness (IF), conciseness (CC) and grammaticality (GM). The scoring adopts a 5-point scale from 1 (worst) to 5 (best). Table 6 shows the average results. The IF, CC and GM scores of PISCES are significantly better than those of mT5 or mBART50, demonstrating the effectiveness of our model. Case Study. Table 7 shows an example Turkish document, the generated summary and the ground truth summary. Though the summary generated by PISCES contains a repeated sentence, it has good overlaps with the ground truth. But for mBART-50, the generated summary is not relevant to the core idea of the document. This observation indicates that, through the cross-lingual and task-specific pre-training, PISCES could better transfer the task knowledge from high-resource directions to zeroshot ones, and even has the ability to generate summaries for the documents whose language does not occur in the fine-tuning stage. Error Analysis. To further study how future research could advance M2MS, we take a closer look at the generation errors of PISCES and analyze them in Appendix F