Papers by Ankan Mullick
Using Sentence-level Classification Helps Entity Extraction from Material Science Literature (2022.lrec-1)
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| Challenge: | Material Science research articles are a rich source of information about entities related to material science. |
| Approach: | They propose to use a sentence-level classifier to identify sentences containing at least one entity mention . they then apply the information extraction models only on the filtered sentences to extract various entities of interest. |
| Outcome: | The proposed model improves the F1 score by more than 4% . the proposed model removes redundant sentences from the articles that contain informative entities . |
A Framework to Generate High-Quality Datapoints for Multiple Novel Intent Detection (2022.findings-naacl)
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| Challenge: | Existing approaches to detect novel intents have been tested in the last decade. |
| Approach: | They propose a framework to detect multiple novel intents with budgeted human annotation cost. |
| Outcome: | The proposed framework outperforms baseline methods in terms of accuracy and F1-score on a set of benchmark datasets. |
Introducing Spotlight: A Novel Approach for Generating Captivating Key Information from Documents (2025.emnlp-main)
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Ankan Mullick, Sombit Bose, Rounak Saha, Ayan Kumar Bhowmick, Aditya Vempaty, Prasenjit Dey, Ravi Kokku, Pawan Goyal, Niloy Ganguly
| Challenge: | Unlike highlights (fragmented key points) and traditional summaries, spotlights selectively emphasize intriguing content to foster deeper reader engagement with the source material. |
| Approach: | They propose a novel paradigm for information extraction that selectively emphasizes intriguing content to foster deeper reader engagement with the source material. |
| Outcome: | The proposed model improves readability and boosts engagement value of the original document. |
A Pointer Network-based Approach for Joint Extraction and Detection of Multi-Label Multi-Class Intents (2024.findings-emnlp)
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| Challenge: | Existing research focuses on simple queries with a single intent, lacking effective systems for handling complex queries with multiple intents. |
| Approach: | They propose a multi-label multi-class intent detection dataset curated from existing benchmarks and a pointer network-based architecture to extract intent spans and detect multiple intents with coarse and fine-grained labels in the form of sextuplets. |
| Outcome: | The proposed system outperforms baseline approaches in terms of accuracy and F1-score. |
On The Persona-based Summarization of Domain-Specific Documents (2024.findings-acl)
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Ankan Mullick, Sombit Bose, Rounak Saha, Ayan Bhowmick, Pawan Goyal, Niloy Ganguly, Prasenjit Dey, Ravi Kokku
| Challenge: | In an ever-expanding world of domain-specific knowledge, summarization of information is a complex task . persona-based summarizing of domain specific information by humans is deemed not preferred . |
| Approach: | They propose a framework for efficient training of a small foundation LLM on a healthcare corpus. |
| Outcome: | The proposed framework fine-tunes a domain-specific small foundation LLM using a healthcare corpus and evaluates its quality using AI-based critiquing. |
Text Takes Over: A Study of Modality Bias in Multimodal Intent Detection (2025.emnlp-main)
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| Challenge: | a new study examines the effectiveness of large language models and non-LLMs in multimodal intent detection . large-scale multimodal data integrations include text, audio, and visual inputs . |
| Approach: | They propose a framework to debias multimodal intent detection datasets by using human evaluation. |
| Outcome: | The proposed framework debiases the datasets and shows that mistral-7B outperforms most competitive models by approximately 9% on MIntRec-1 and 4% on MIndRec2.0. |
An Evaluation Framework for Legal Document Summarization (2022.lrec-1)
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| Challenge: | Existing metrics for summarizing legal documents fail to evaluate intent in the original text. |
| Approach: | They propose an automated intent-based summarization metric which shows a better agreement with human evaluation as compared to other automated metrics like BLEU, ROUGE-L etc. |
| Outcome: | The proposed method shows that human evaluation is more accurate than other metrics. |
Intent Identification and Entity Extraction for Healthcare Queries in Indic Languages (2023.findings-eacl)
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| Challenge: | Currently, there is a lack of data and technology for resource-poor languages in developing countries like India. |
| Approach: | They propose to use two different datasets to analyze query intents and entities in healthcare. |
| Outcome: | The proposed model is useful to identify query intents and entities in real-world scenarios. |