In e-commerce, opinion summarization is the process of condensing the opinions presented in product reviews. However, the absence of large amounts of supervised datasets presents challenges in generating both aspect-specific and general opinion summaries. Existing approaches have attempted to address these challenges through synthetic dataset creation (SDC). However, general opinion summarization models struggle to generate summaries faithful to the input reviews whereas aspect-specific opinion summarization models are limited due to their reliance on human-specified aspects and seed words. To address this, we propose SDC strategies tailored for general and aspect-specific opinion summarization. We experimented on three e-commerce test sets: Oposum+, Amazon, and Flipkart. For general opinion summarization, pre-trained language model (PLM) fine-tuned on our general synthetic dataset surpass the SOTA on average by 2.3 R1 points. Faithfulness evaluation metrics and human evaluations indicate that our model-generated summaries are more faithful to the input compared to others. For aspect-specific opinion summarization, PLM fine-tuned on our aspect-specific synthetic dataset surpass SOTA by ~ 1 R1 point without the aid of any human-specified aspects or seed words.

The democratization of e-commerce platforms has moved an increasingly diversified Indian user base to shop online. We have deployed reliable and precise large-scale Machine Translation systems for several Indian regional languages in this work. Building such systems is a challenge because of the low-resource nature of the Indian languages. We develop a structured model development pipeline as a closed feedback loop with external manual feedback through an Active Learning component. We show strong synthetic parallel data generation capability and consistent improvements to the model over iterations. Starting with 1.2M parallel pairs for English-Hindi we have compiled a corpus with 400M+ synthetic high quality parallel pairs across different domains. Further, we need colloquial translations to preserve the intent and friendliness of English content in regional languages, and make it easier to understand for our users. We perform robust and effective domain adaptation steps to achieve colloquial such translations. Over iterations, we show 9.02 BLEU points improvement for English to Hindi translation model. Along with Hindi, we show that the overall approach and best practices extends well to other Indian languages, resulting in deployment of our models across 7 Indian Languages.