Leveraging large language models (LLMs) for query expansion has proven highly effective across diverse tasks and languages. Yet, challenges remain in optimizing query formatting and prompting, often with less focus on handling retrieval results. In this paper, we introduce Multi-query Multi-passage Late Fusion (MMLF), a straightforward yet potent pipeline that generates sub-queries, expands them into pseudo-documents, retrieves them individually, and aggregates results using reciprocal rank fusion. Our experiments demonstrate that MMLF exhibits superior performance across five BEIR benchmark datasets, achieving an average improvement of 4% and a maximum gain of up to 8% in both Recall@1k and nDCG@10 compared to state of the art across BEIR information retrieval datasets.

In this paper, we address a novel task, Multiple TimeLine Summarization (MTLS), which extends the flexibility and versatility of Time-Line Summarization (TLS). Given any collection of time-stamped news articles, MTLS automatically discovers important yet different stories and generates a corresponding time-line for each story. To achieve this, we propose a novel unsupervised summarization framework based on two-stage affinity propagation. We also introduce a quantitative evaluation measure for MTLS based on previousTLS evaluation methods. Experimental results show that our MTLS framework demonstrates high effectiveness and MTLS task can give bet-ter results than TLS.