Papers by Siddharth N
AUTOSUMM: A Comprehensive Framework for LLM-Based Conversation Summarization (2025.acl-industry)
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Abhinav Gupta, Devendra Singh, Greig A Cowan, N Kadhiresan, Siddharth Srivastava, Yagneswaran Sriraja, Yoages Kumar Mantri
| Challenge: | Large language models (LLMs) are used to summarize large volumes of textual information into a smaller, more manageable size. |
| Approach: | They propose a large language model-based summarization system for regulated banking environments that generates accurate, privacy-compliant summaries of customer-advisor conversations. |
| Outcome: | The proposed system achieves 94% factual consistency rate and significant reduction in hallucination rate. |
Multi-Label Classification for Implicit Discourse Relation Recognition (2024.findings-acl)
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| Challenge: | Prior research in discourse relation recognition has treated these instances as separate examples during training, with a gold-standard prediction matching one of the labels considered correct at test time. |
| Approach: | They propose to use multiple labels to annotate an example when multiple relations are believed to hold simultaneously. |
| Outcome: | The proposed frameworks don't depress performance for single-label prediction. |
StrAE: Autoencoding for Pre-Trained Embeddings using Explicit Structure (2023.emnlp-main)
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| Challenge: | Structured Autoencoder framework StrAE enables effective learning of multi-level representations through strict adherence to explicit structure. |
| Approach: | They propose a Structured Autoencoder framework that strictly adheres to explicit structure and uses a contrastive objective over tree-structured representations. |
| Outcome: | The proposed framework outperforms baselines that don’t involve explicit hierarchical compositions and is comparable to models given informative structure. |