Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this work  investigate the application of entity type models in extractive multi-document#R##N#summarization using the automatic caption generation for images of geo-located entities (e.g.#R##N#stminster Abbey, Loch Ness, Eiffel Tor) as an application scenario. Entity type models#R##N#contain sets of patterns aiming to capture the ways the geo-located entities are described in natural#R##N#language. They are automatically derived from texts about geo-located entities of the same#R##N#type (e.g. churches, lakes, tors).  collect texts about geo-located entities from Wikipedia#R##N#because our investigation show that the information humans associate with entity types positively#R##N#correlates with the information contained in Wikipedia articles about the same entity#R##N#types.#R##N##R##N# integrate entity type models into a multi-document summarizer and use them to address the#R##N#two major tasks in extractive multi-document summarization: sentence scoring and summary#R##N#composition.  experiment with three different representation methods for entity type models:#R##N#signature words, n-gram language models and dependency patterns.  first propose that#R##N#entity type models will improve sentence scoring, i.e. they will help to assign higher scores to#R##N#sentences which are more relevant to the output summary than to those which are not. Secondly,#R##N# claim that summary composition can be improved using entity type models.#R##N##R##N# follow two different approaches to integrate the entity type models into our multi-document#R##N#summarizer. In the first approach  use the entity type models in combination with existing#R##N#standard summarization features to score the sentences.  also manually categorize the set#R##N#of patterns by the information types they describe and use them to reduce redundancy and to#R##N#produce better flow within the summary. The second approach aims to eliminate the need for#R##N#manual intervention and to fully automate the process of summary generation. As in the first#R##N#approach the sentences are scored using standard summarization features and entity type models.#R##N##R##N#Hover, unlike the first approach  fully automate the process of summary composition by#R##N#simultaneously addressing the redundancy and flow aspects of the summary.#R##N##R##N# evaluate the summarizer with integrated entity type models relative to (1) a summarizer using#R##N#standard text related features commonly used in summarization and (2) the Wikipedia location#R##N#descriptions. The latter constitute a strong baseline for automated summaries to be evaluated#R##N#against. The automated summaries are evaluated against human reference summaries using#R##N#ROUGE and human readability evaluation, as is a common practice in automatic summarization.#R##N##R##N#Our results show that entity type models significantly improve the quality of output summaries#R##N#over that of summaries generated using standard summarization features andWikipedia baseline#R##N#summaries. The representation of entity type models using dependency patterns is superior to#R##N#the representations using signature words and n-gram language models.
['Ahmet Aker']
I am writing a research paper on a idea Entity Type Modeling for Multi-Document Summarization: Generating Descriptive Summaries of Geo-Located Entities
[{"from": "human", "value": "I am writing a research paper on a idea Entity Type Modeling for Multi-Document Summarization: Generating Descriptive Summaries of Geo-Located Entities"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this work  investigate the application of entity type models in extractive multi-document#R##N#summarization using the automatic caption generation for images of geo-located entities (e.g.#R##N#stminster Abbey, Loch Ness, Eiffel Tor) as an application scenario. Entity type models#R##N#contain sets of patterns aiming to capture the ways the geo-located entities are described in natural#R##N#language. They are automatically derived from texts about geo-located entities of the same#R##N#type (e.g. churches, lakes, tors).  collect texts about geo-located entities from Wikipedia#R##N#because our investigation show that the information humans associate with entity types positively#R##N#correlates with the information contained in Wikipedia articles about the same entity#R##N#types.#R##N##R##N# integrate entity type models into a multi-document summarizer and use them to address the#R##N#two major tasks in extractive multi-document summarization: sentence scoring and summary#R##N#composition.  experiment with three different representation methods for entity type models:#R##N#signature words, n-gram language models and dependency patterns.  first propose that#R##N#entity type models will improve sentence scoring, i.e. they will help to assign higher scores to#R##N#sentences which are more relevant to the output summary than to those which are not. Secondly,#R##N# claim that summary composition can be improved using entity type models.#R##N##R##N# follow two different approaches to integrate the entity type models into our multi-document#R##N#summarizer. In the first approach  use the entity type models in combination with existing#R##N#standard summarization features to score the sentences.  also manually categorize the set#R##N#of patterns by the information types they describe and use them to reduce redundancy and to#R##N#produce better flow within the summary. The second approach aims to eliminate the need for#R##N#manual intervention and to fully automate the process of summary generation. As in the first#R##N#approach the sentences are scored using standard summarization features and entity type models.#R##N##R##N#Hover, unlike the first approach  fully automate the process of summary composition by#R##N#simultaneously addressing the redundancy and flow aspects of the summary.#R##N##R##N# evaluate the summarizer with integrated entity type models relative to (1) a summarizer using#R##N#standard text related features commonly used in summarization and (2) the Wikipedia location#R##N#descriptions. The latter constitute a strong baseline for automated summaries to be evaluated#R##N#against. The automated summaries are evaluated against human reference summaries using#R##N#ROUGE and human readability evaluation, as is a common practice in automatic summarization.#R##N##R##N#Our results show that entity type models significantly improve the quality of output summaries#R##N#over that of summaries generated using standard summarization features andWikipedia baseline#R##N#summaries. The representation of entity type models using dependency patterns is superior to#R##N#the representations using signature words and n-gram language models."}]