Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The TIPSTER Text Summarization Evaluation (SUMMAC) has developed several new extrinsic and intrinsic methods for evaluating summaries. It has established definitively that automatic text summarization is very effective in relevance assessment tasks on news articles. Summaries as short as 17% of full text length sped up decision-making by almost a factor of 2 with no statistically significant degradation in accuracy. Analysis of feedback forms filled in after each decision indicated that the intelligibility of present-day machine-generated summaries is high. Systems that performed most accurately in the production of indicative and informative topic-related summaries used term frequency and co-occurrence statistics, and vocabulary overlap comparisons beten text passages. Hover, in the absence of a topic, these statistical methods do not appear to provide any additional leverage: in the case of generic summaries, the systems re indistinguishable in accuracy. The paper discusses some of the tradeoffs and challenges faced by the evaluation, and also lists some of the lessons learned, impacts, and possible future directions. The evaluation methods used in the SUMMAC evaluation are of interest to both summarization evaluation as ll as evaluation of other ‘output-related’ NLP technologies, where there may be many potentially acceptable outputs, with no automatic way to compare them.
['Inderjeet Mani', 'Gary L. Klein', 'David House', 'Lynette Hirschman', 'Therese Firmin', 'Beth M. Sundheim']
I am writing a research paper on a idea SUMMAC: a text summarization evaluation
[{"from": "human", "value": "I am writing a research paper on a idea SUMMAC: a text summarization evaluation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The TIPSTER Text Summarization Evaluation (SUMMAC) has developed several new extrinsic and intrinsic methods for evaluating summaries. It has established definitively that automatic text summarization is very effective in relevance assessment tasks on news articles. Summaries as short as 17% of full text length sped up decision-making by almost a factor of 2 with no statistically significant degradation in accuracy. Analysis of feedback forms filled in after each decision indicated that the intelligibility of present-day machine-generated summaries is high. Systems that performed most accurately in the production of indicative and informative topic-related summaries used term frequency and co-occurrence statistics, and vocabulary overlap comparisons beten text passages. Hover, in the absence of a topic, these statistical methods do not appear to provide any additional leverage: in the case of generic summaries, the systems re indistinguishable in accuracy. The paper discusses some of the tradeoffs and challenges faced by the evaluation, and also lists some of the lessons learned, impacts, and possible future directions. The evaluation methods used in the SUMMAC evaluation are of interest to both summarization evaluation as ll as evaluation of other \u2018output-related\u2019 NLP technologies, where there may be many potentially acceptable outputs, with no automatic way to compare them."}]