Search Results for author: Litton J Kurisinkel

Found 11 papers, 1 papers with code

Attention-based Neural Text Segmentation

1 code implementation29 Aug 2018 Pinkesh Badjatiya, Litton J Kurisinkel, Manish Gupta, Vasudeva Varma

Text segmentation plays an important role in various Natural Language Processing (NLP) tasks like summarization, context understanding, document indexing and document noise removal.

Feature Engineering Segmentation +3

SSAS: Semantic Similarity for Abstractive Summarization

no code implementations IJCNLP 2017 Raghuram Vadapalli, Litton J Kurisinkel, Manish Gupta, Vasudeva Varma

Ideally a metric evaluating an abstract system summary should represent the extent to which the system-generated summary approximates the semantic inference conceived by the reader using a human-written reference summary.

Abstractive Text Summarization Natural Language Inference +2

Set to Ordered Text: Generating Discharge Instructions from Medical Billing Codes

no code implementations IJCNLP 2019 Litton J Kurisinkel, Nancy Chen

This task differs from other natural language generation tasks in the following ways: (1) The input is a set of identifiable entities (ICD codes) where the relations between individual entity are not explicitly specified.

Recipe Generation Sentence +1

Coherent and Concise Radiology Report Generation via Context Specific Image Representations and Orthogonal Sentence States

no code implementations NAACL 2021 Litton J Kurisinkel, Ai Ti Aw, Nancy F Chen

Neural models for text generation are often designed in an end-to-end fashion, typically with zero control over intermediate computations, limiting their practical usability in downstream applications.

Informativeness Sentence +1

LLM Based Multi-Document Summarization Exploiting Main-Event Biased Monotone Submodular Content Extraction

no code implementations5 Oct 2023 Litton J Kurisinkel, Nancy F. Chen

Multi-document summarization is a challenging task due to its inherent subjective bias, highlighted by the low inter-annotator ROUGE-1 score of 0. 4 among DUC-2004 reference summaries.

Document Summarization Informativeness +3

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