Social Network Post Title Selection via Sentence Weighting
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Solution Overview
Problem
Existing methods for determining the title of social network posts, especially in platforms like Twitter and Facebook, face challenges due to the lack of rich format or metadata information, often resulting in noisy and irrelevant content.
Innovation Solution
The approach utilizes term frequency-inverse document frequency (TF-IDF) scores and natural-language processing techniques to analyze and clean the text, assign part-of-speech tags, and calculate sentence weights to select the most relevant sentence as the title, effectively filtering out noise and improving title accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If document format or metadata fields are used to determine the title, then the process is simple, but the title quality is poor due to lack of rich format information and noisy content in social media posts
Solution Approach 1:
The patent segments the document into multiple sentences and evaluates each sentence independently to determine its suitability as a title. This is achieved by dividing the document text into individual sentences and calculating relevance scores for each sentence based on keyword matching and positional weights, rather than treating the entire document as a single unit.
Solution Approach 2:
The patent introduces an intermediary scoring mechanism that evaluates sentences based on multiple factors including keyword relevance, sentence position, and length. This intermediary evaluation process mediates between the raw document content and the final title selection, using a calculated relevance score to identify the most appropriate title candidate.
2Measurement precision
If natural-language processing techniques are used to analyze and clean text, then title quality improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by selectively processing only the necessary components of the text - specifically extracting and evaluating individual sentences rather than performing comprehensive natural language processing on the entire document. The cleaning and analysis are applied partially to achieve sufficient title accuracy without the full overhead of complete NLP processing.
Solution Approach 2:
The patent changes parameters by adjusting the weighting factors for different evaluation criteria (keyword relevance, sentence position, sentence length) to optimize the balance between processing speed and title accuracy. By tuning these parameters, the system can adapt to different performance requirements while maintaining effective title generation.
3Measurement precision
If sentence weights are calculated based on multiple factors, then the most relevant sentence is selected as title, but the calculation complexity increases
Solution Approach 1:
The patent segments the title evaluation into distinct weighted components: keyword relevance score, sentence position weight, and sentence length consideration. Each component is calculated separately and then combined through a linear weighting formula, making the complex evaluation process more manageable and interpretable.
Solution Approach 2:
The patent merges multiple evaluation factors into a single综合 relevance score using a weighted summation approach. The final title selection is based on combining the keyword matching score, positional weight, and length factor into one unified metric that ranks candidate sentences for title selection.
Data Source
AI summary
Methods, systems, and computer programs are presented for selecting a title for a post of a social network. A method includes operations for identifying a word weight for each word in a corpus based on the relevance of each word, receiving a document that includes several sentences, and assigning to each word in the sentences a tag indicative of a part of speech. Further, the method includes operations for calculating a topic weight for each sentence based on the tags of the words in the sentence, and for calculating a sentence weight for each sentence based on a sum of the word weights of the words in the sentence, the topic weight for the sentence, and a position weight of the sentence within a paragraph. The sentence with the highest sentence weight is selected as the title for the document, and the title is presented with the document to a user.