Semantic IDs (SIDs) compress item embeddings into discrete code sequences used as the item vocabulary in generative retrieval. If the encoder is multilingual, it is tempting to assume the resulting SIDs are language-agnostic. We test this assumption on Amazon ESCI dataset (CITATION) rendered in English, Spanish, and Japanese. It fails at two separate points. Multilingual E5 places translations of the same product measurably apart, and residual quantization converts that displacement into a discrete prefix failure: under an English-heavy fit, a Japanese translation recovers the first SID code of its English counterpart in only 7.7% of cases, against 89.0% for an English rewording. A product-directed control shows the quantizer does not selectively increase language directions, instead the damage originates in the encoder. Most surprisingly, fitting the quantizer on a balanced language mixture makes cross-lingual agreement worse, not better, even as it equalizes codebook use. An English only fit preserves the first code for 67.6% of Spanish translations, the balanced fit preserves it for 6.6%. The results show that multilingual exposure alone does not buy consistent SIDs across languages. Cross-lingual SID agreement has to be optimized directly, or retrieval must tolerate code variation.

Quantum Natural Language Processing (QNLP) is an emerging interdisciplinary field at the intersection of quantum computing, natural language understanding, and formal linguistic theory. As advances in quantum hardware and algorithms accelerate, QNLP promises new paradigms for representation learning, semantic modeling, and efficient computation. However, existing literature remains fragmented, with no unified synthesis across modeling, encoding, and evaluation dimensions. In this work, we present the first systematic and taxonomy driven survey of QNLP that holistically organizes research spanning three core dimensions: computational models, encoding paradigms, and evaluation frameworks. First, we analyze foundational approaches that map linguistic structures into quantum formalism, including categorical compositional models, variational quantum circuits, and hybrid quantum classical architectures. Second, we introduce a unified taxonomy of encoding strategies, ranging from quantum tokenization and state preparation to embedding based encodings, highlighting tradeoffs in scalability, noise resilience, and expressiveness. Third, we provide the first comparative synthesis of evaluation methodologies, benchmark datasets, and performance metrics, while identifying reproducibility and standardization gaps.We further contrast quantum inspired NLP methods with fully quantum implemented systems, offering insights into resource efficiency, hardware feasibility, and real world applicability. Finally, we outline open challenges such as integration with LLMs and unified benchmark design, and propose a research agenda for advancing QNLP as a scalable and reliable discipline.

Despite tremendous progress in automatic summarization, state-of-the-art methods are predominantly trained to excel in summarizing short newswire articles, or documents with strong layout biases such as scientific articles or government reports. Efficient techniques to summarize financial documents, discussing facts and figures, have largely been unexplored, majorly due to the unavailability of suitable datasets. In this work, we present ECTSum, a new dataset with transcripts of earnings calls (ECTs), hosted by publicly traded companies, as documents, and experts-written short telegram-style bullet point summaries derived from corresponding Reuters articles. ECTs are long unstructured documents without any prescribed length limit or format. We benchmark our dataset with state-of-the-art summarization methods across various metrics evaluating the content quality and factual consistency of the generated summaries. Finally, we present a simple yet effective approach, ECT-BPS, to generate a set of bullet points that precisely capture the important facts discussed in the calls.