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--- |
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language: |
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- en |
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tags: |
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- summarization |
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license: mit |
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datasets: |
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- multi_news |
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model-index: |
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- name: ppiiesle3y/fined-tuned-bart |
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results: |
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- task: |
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type: summarization |
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name: Summarization |
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dataset: |
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name: multi_news |
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type: multi_news |
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split: train |
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metrics: |
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- name: ROUGE-1 |
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type: rouge |
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value: 43.7065 |
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verified: true |
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- name: ROUGE-2 |
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type: rouge |
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value: 16.5533 |
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verified: true |
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- name: ROUGE-L |
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type: rouge |
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value: 24.7588 |
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verified: true |
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- name: ROUGE-LSUM |
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type: rouge |
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value: 37.7586 |
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verified: true |
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- name: loss |
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type: loss |
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value: 2.00663 |
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verified: true |
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- name: gen_len |
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type: gen_len |
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value: 129.1379 |
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verified: true |
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--- |
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# TL;DR AT2 Applied Natural Language Processing Assignment |
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## PROJECT OBJECTIVES |
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This project aims to use NLP technology to summarise longer passages of text into succinct and accurate summations. |
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## PROJECT OUTCOMES AND INSIGHTS |
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The expected outcomes from the project is a model that is able to intake a larger body of text and provide a shortened summary that is both succinct and accurate. This will benefit most human readers by making it more efficient gain understanding from written text. Applications for this technology include as a study aide, for people in roles where they are required to quickly assess documents such as book publishers reading through manuscripts to assess if they are fit for publishing or script readers etc. |
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The most significant impact this project has is to increase information assimilation in a compressed timeframe, thus saving time. |
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