Papers by Somnath Basu Roy Chowdhury
Unsupervised Extractive Opinion Summarization Using Sparse Coding (2022.acl-long)
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| Challenge: | Existing methods for opinion summarization rely on human annotations, which may not be feasible. |
| Approach: | They propose to perform opinion summarization in an unsupervised manner by using a dictionary learning algorithm that implicitly captures semantic information from the review text. |
| Outcome: | The proposed algorithm performs well on SPACE and AMAZON datasets and performs controllable summarization to generate aspect-specific summaries using only a few samples. |
Adversarial Scrubbing of Demographic Information for Text Classification (2021.emnlp-main)
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Somnath Basu Roy Chowdhury, Sayan Ghosh, Yiyuan Li, Junier Oliva, Shashank Srivastava, Snigdha Chaturvedi
| Challenge: | Existing frameworks to debias contextual representations can encode undesirable attributes, like demographic associations of the users, while being trained for an unrelated task. |
| Approach: | They propose an adversarial learning framework to debias contextual representations by encoding undesirable attributes while being trained for an unrelated task. |
| Outcome: | The proposed framework debiases representations on 8 datasets while remaining informative on the target task. |
Learning beyond Datasets: Knowledge Graph Augmented Neural Networks for Natural Language Processing (N18-1)
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| Challenge: | Currently, machine learning is limited in scalability and is limited to specific training data. |
| Approach: | They propose to enhance learning models with world knowledge in the form of Knowledge Graph fact triples for natural language processing tasks. |
| Outcome: | The proposed method is highly scalable to the amount of prior information that has to be processed and can be applied to any generic NLP task. |
Instance-based Inductive Deep Transfer Learning by Cross-Dataset Querying with Locality Sensitive Hashing (D19-61)
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| Challenge: | Existing methods to train supervised learning models rely on labeled data, which is expensive or impossible to acquire. |
| Approach: | They propose an inductive transfer learning method that can augment learning models by infusing similar instances from different learning tasks in Natural Language Processing domain. |
| Outcome: | The proposed method improves the performance of three major news classification datasets by reducing dependency on labeled data by a significant margin. |
Unsupervised Opinion Summarization Using Approximate Geodesics (2023.findings-emnlp)
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| Challenge: | Existing methods for opinion summarization are limited due to the scarcity of data. |
| Approach: | They propose a system to perform unsupervised extractive opinion summarization using a dictionary-based representation learning model that generates topical representations of texts. |
| Outcome: | The proposed system achieves strong performance on three opinion summarization datasets. |
Exploring Safety-Utility Trade-Offs in Personalized Language Models (2025.naacl-long)
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| Challenge: | Prior studies have shown that large language models can exhibit bias against specific demographic groups and engage in the generation of stereotypical responses. |
| Approach: | They propose a framework to evaluate LLM performance along two axes: safety and utility. |
| Outcome: | The proposed framework evaluates the performance of LLMs along two axes: safety and utility. |
Read Top News First: A Document Reordering Approach for Multi-Document News Summarization (2022.findings-acl)
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Chao Zhao, Tenghao Huang, Somnath Basu Roy Chowdhury, Muthu Kumar Chandrasekaran, Kathleen McKeown, Snigdha Chaturvedi
| Challenge: | Existing methods for extracting multi-document news summarization neglect relative importance of documents. |
| Approach: | They propose to concatenate all documents into a single meta-document and then summarize it using an SDS model. |
| Outcome: | The proposed approach outperforms state-of-the-art methods with more complex architectures. |
Is Everything in Order? A Simple Way to Order Sentences (2021.emnlp-main)
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| Challenge: | Existing work on sentence ordering has focused on exploiting different categories of features like coreference clues. |
| Approach: | They propose a sentence ordering task as a conditional text-to-marker generation problem that leverages a pre-trained Transformer-based model to identify a coherent order for a given set of shuffled sentences. |
| Outcome: | The proposed model performs well across 7 datasets in Perfect Match Ratio and Kendall’s tau. |
Aspect-aware Unsupervised Extractive Opinion Summarization (2023.findings-acl)
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| Challenge: | Extractive opinion summarization extracts sentences from reviews to represent the prevalent opinions about a product or service. |
| Approach: | They propose a method for unsupervised extractive opinion summarization that automatically identifies the aspects described in review sentences and extracts sentences based on their aspects. |
| Outcome: | The proposed method improves aspect coverage and performs well on multiple opinion summarization datasets. |