Litton J Kurisinkel

Also published as: Litton J Kurisinkel


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Coherent and Concise Radiology Report Generation via Context Specific Image Representations and Orthogonal Sentence States
Litton J Kurisinkel | Ai Ti Aw | Nancy F Chen
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers

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. In this work, we incorporate explicit means into neural models to ensure topical continuity, informativeness and content diversity of generated radiology reports. For the purpose we propose a method to compute image representations specific to each sentential context and eliminate redundant content by exploiting diverse sentence states. We conduct experiments to generate radiology reports from medical images of chest x-rays using MIMIC-CXR. Our model outperforms baselines by up to 18% and 29% respective in the evaluation for informativeness and content ordering respectively, relative on objective metrics and 16% on human evaluation.


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Set to Ordered Text: Generating Discharge Instructions from Medical Billing Codes
Litton J Kurisinkel | Nancy Chen
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We present set to ordered text, a natural language generation task applied to automatically generating discharge instructions from admission ICD (International Classification of Diseases) codes. 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. (2) The output text is not a narrative description (e.g. news articles) composed from the input. Rather, inferences are made from the input (symptoms specified in ICD codes) to generate the output (instructions). (3) There is an optimal order in which each sentence (instruction) should appear in the output. Unlike most other tasks, neither the input (ICD codes) nor their corresponding symptoms appear in the output, so the ordering of the output instructions needs to be learned in an unsupervised fashion. Based on clinical intuition, we hypothesize that each instruction in the output is mapped to a subset of ICD codes specified in the input. We propose a neural architecture that jointly models (a) subset selection: choosing relevant subsets from a set of input entities; (b) content ordering: learning the order of instructions; and (c) text generation: representing the instructions corresponding to the selected subsets in natural language. In addition, we penalize redundancy during beam search to improve tractability for long text generation. Our model outperforms baseline models in BLEU scores and human evaluation. We plan to extend this work to other tasks such as recipe generation from ingredients.


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EquGener: A Reasoning Network for Word Problem Solving by Generating Arithmetic Equations
Pruthwik Mishra | Litton J Kurisinkel | Dipti Misra Sharma | Vasudeva Varma
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation


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Abstractive Multi-document Summarization by Partial Tree Extraction, Recombination and Linearization
Litton J Kurisinkel | Yue Zhang | Vasudeva Varma
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Existing work for abstractive multidocument summarization utilise existing phrase structures directly extracted from input documents to generate summary sentences. These methods can suffer from lack of consistence and coherence in merging phrases. We introduce a novel approach for abstractive multidocument summarization through partial dependency tree extraction, recombination and linearization. The method entrusts the summarizer to generate its own topically coherent sequential structures from scratch for effective communication. Results on TAC 2011, DUC-2004 and 2005 show that our system gives competitive results compared with state of the art abstractive summarization approaches in the literature. We also achieve competitive results in linguistic quality assessed by human evaluators.

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SSAS: Semantic Similarity for Abstractive Summarization
Raghuram Vadapalli | Litton J Kurisinkel | Manish Gupta | Vasudeva Varma
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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. Most of the previous approaches relied upon word or syntactic sub-sequence overlap to evaluate system-generated summaries. Such metrics cannot evaluate the summary at semantic inference level. Through this work we introduce the metric of Semantic Similarity for Abstractive Summarization (SSAS), which leverages natural language inference and paraphrasing techniques to frame a novel approach to evaluate system summaries at semantic inference level. SSAS is based upon a weighted composition of quantities representing the level of agreement, contradiction, independence, paraphrasing, and optionally ROUGE score between a system-generated and a human-written summary.


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Non-decreasing Sub-modular Function for Comprehensible Summarization
Litton J Kurisinkel | Pruthwik Mishra | Vigneshwaran Muralidaran | Vasudeva Varma | Dipti Misra Sharma
Proceedings of the NAACL Student Research Workshop


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A Sandhi Splitter for Malayalam
Devadath V V | Litton J Kurisinkel | Dipti Misra Sharma | Vasudeva Varma
Proceedings of the 11th International Conference on Natural Language Processing