Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Anand Kumar, A.K.

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    Advancing Human-Like Summarization: Approaches to Text Summarization
    (CEUR-WS, 2023) Gowhar, S.; Sharma, B.; Gupta, A.K.; Anand Kumar, A.K.
    Text summarization, a well-explored domain within Natural Language Processing, has witnessed significant progress. The ILSUM shared task, encompassing various languages, such as English, Hindi, Gujarati, and Bengali, concentrates on text summarization. The proposed research focuses on leveraging pretrained sequence-to-sequence models for abstractive summarization specifically in the context of the English language. This paper provides an extensive exposition of our model and approach. Notably, we achieved the top ranking in the English Language subtask. Furthermore, this paper dives into an analysis of various techniques for extractive summarization, presenting their outcomes and drawing comparisons with abstractive summarization. © 2023 Copyright for this paper by its authors.
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    Prediction of High-Resolution Atmospheric CO2 Concentration from OCO-2 using Machine Learning
    (Association for Computing Machinery, 2023) Pais, S.M.; Bhattacharjee, S.; Anand Kumar, A.K.
    Carbon Dioxide (CO2) is a greenhouse gas (GHG) emitted by human anthropogenic activities. The satellite measurement of atmospheric column-averaged CO2 concentration (XCO2) provides an excellent opportunity to understand the global carbon cycle for a large comprehensive temporal range. Orbiting Carbon Observatory-2 (OCO-2) satellite provides highly accurate data with a spatial resolution of approximately 3 km2. However, OCO-2 measures one location on the Earth's surface almost fortnightly. Also, the clouds and aerosols cause missing data. In this work, the OCO-2 measurements, along with Open-Source Data Inventory for Anthropogenic CO2 (ODIAC) emission estimate, are considered. A spatial upscaling, followed by different machine learning methods are used to predict high-resolution, continuous mapping of XCO2. The prediction models are evaluated using the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) for Germany, considering a temporal range of November 2018 to December 2019. The least error is attained by monthly model, achieving a MAE and RMSE of 0.707 ppm and 1.187 ppm, respectively, using the extremely randomized trees (ERT) method. The predictions are externally validated using Total Carbon Column Observing Network (TCCON) ground-based measurements as well. © 2023 ACM.