Papers by Kundan Krishna
Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques (2021.acl-long)
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| Challenge: | Creating digital SOAP notes is burdensome and contributes to physician burnout . authors propose a pipeline to generate these notes based on transcripts of clinical conversations . |
| Approach: | They propose a pipeline to leverage deep summarization models based on conversations between physicians and patients . they propose an algorithm that extracts important utterances relevant to each section and generates one summary sentence per cluster . |
| Outcome: | The proposed algorithm outperforms its abstract counterpart by 8 ROUGE-1 points and produces more factual sentences as assessed by human evaluators. |
Generating Topic-Oriented Summaries Using Neural Attention (N18-1)
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| Challenge: | Existing summarization algorithms generate a single summary and are not capable of generating multiple summaries tuned to the interests of the readers. |
| Approach: | They propose an attention based RNN framework to generate multiple summaries tuned to different topics of interest. |
| Outcome: | The proposed framework outperforms baselines and shows that attention bias can be successfully used to generate topic-tuned summaries. |
Does Pretraining for Summarization Require Knowledge Transfer? (2021.findings-emnlp)
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| Challenge: | Existing theories claim that pretraining models learn linguistic knowledge from the pretraining corpus, but scientific explanations for these benefits remain unknown. |
| Approach: | They propose to use random character n-grams to test models on real corpora to see if the small residual benefit of using real data could be accounted for by the structure of the pretraining task. |
| Outcome: | The proposed task performs on documents consisting of character n-grams, whereas pretrained models perform on real corpora with no residual benefit. |
Improving the Robustness of Summarization Models by Detecting and Removing Input Noise (2023.findings-emnlp)
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| Challenge: | Abstractive summarization models are typically evaluated using test data that is identically distributed as training data. |
| Approach: | They propose a method to detect and remove input noise from documents to be summarized without extra training or auxiliary models. |
| Outcome: | The proposed method recovers a large fraction of the loss in performance, sometimes as large as 11 ROUGE-1 points, without extra training, auxiliary models, or prior knowledge of the type of noise. |
Corpus-based Content Construction (C18-1)
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| Challenge: | Existing work in this direction focuses on generating content for standard platforms like Wikipedia, where the content style is fairly consistent, but there could be multiple representations of the same information across the repository. |
| Approach: | They propose an automatic approach to generate an initial version of the author’s intended text based on an input content snippet. |
| Outcome: | The proposed approach improves performance against baselines on several metrics. |
Vocabulary Tailored Summary Generation (C18-1)
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| Challenge: | Existing frameworks for summary generation do not account for linguistic preferences of the specific audience who will consume the summary. |
| Approach: | They propose a neural framework to generate summary constrained to a vocabulary-defined linguistic preferences of a target audience. |
| Outcome: | The proposed approach generates understandable summaries with simpler words and readable summary with shorter words against a state-of-the-art word embedding based lexical substitution algorithm. |
Evaluating the Factuality of Zero-shot Summarizers Across Varied Domains (2024.eacl-short)
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| Challenge: | Recent work has shown that large language models can generate zero-shot summaries without explicit supervision that are often comparable or even preferred to manually composed reference summary. |
| Approach: | They evaluate large language models (LLMs) that generate zero-shot summaries without explicit supervision that are often comparable to manual reference summary . they acquire annotations from domain experts to identify inconsistencies in summaires and categorize errors. |
| Outcome: | The proposed model outperforms fine-tuned models in biomedical articles and legal bills across specialized domains. |
USB: A Unified Summarization Benchmark Across Tasks and Domains (2023.findings-emnlp)
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| Challenge: | Existing summarization benchmarks lack the rich annotations needed to address important problems related to control and reliability. |
| Approach: | They propose a Wikipedia-derived summarization benchmark with crowd-sourced annotations . they find that fine-tuned models outperform larger few-shot prompted language models . |
| Outcome: | The proposed model outperforms many-shot prompted language models on multiple tasks . the proposed model is based on Wikipedia annotations and can be used in other domains . |
Downstream Datasets Make Surprisingly Good Pretraining Corpora (2023.acl-long)
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| Challenge: | a dominant practice is to fine tune large pretrained transformer models using smaller downstream datasets . performance gains are not always attributable to the use of external data in massive amounts . |
| Approach: | They propose to use the same (downstream) training data for pretraining and finetuning to compare models. |
| Outcome: | The proposed model outperforms standard pretraining on the BookWiki corpus on 7 and 5 datasets. |