In the fast-paced world of journalism, staying up-to-date and delivering engaging content to readers is crucial. However, with the exponential growth of digital information, journalists often need help with copious amounts of text to sift through. Fortunately, advancements in Artificial Intelligence (AI) have paved the way for powerful techniques that can help streamline summarizing texts and extracting essential vectors or embeddings. In this blog post, we will explore some of the most prominent AI techniques, such as BERT, Spacy’s weighted vectors, Doc2Vec, and Universal Sentence Encoder (USE), that can aid journalists in understanding and summarizing texts effectively. Moreover, we will explore how these techniques can assist in recommending best-fit photographs to enhance their storytelling capabilities.

BERT (Bidirectional Encoder Representations from Transformers): BERT, a groundbreaking natural language processing (NLP model, has revolutionized the field of AI and text understanding. It is a transformer-based model designed to comprehend the context of words in a sentence by considering the preceding and succeeding words. BERT’s bidirectional approach helps it capture intricate relationships between words, leading to more accurate text summarization and representation. Journalists can efficiently condense lengthy articles or reports into concise and coherent summaries by employing BERT for text summarization. The summarization process involves feeding the article into the BERT model, which then outputs a summarized version that retains the important information from the original text.

Spacy Weighted Vectors with TF-IDF Vectorizer: Spacy, a popular NLP library, offers an excellent approach to extracting word-level embeddings. Using the Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer, Spacy assigns weights to words based on their importance in the context of the entire document. This technique enables journalists to understand the significance of specific words in a given text. Additionally, Spacy can generate sentence-level embeddings, representing the overall meaning of a sentence. Journalists can derive a vector representation of the sentence’s content by calculating the average word embeddings within a sentence. These embeddings can compare and match similar sentences across different articles.

Spacy at Word, Sentence, and Paragraph Level Embeddings: Spacy’s capabilities extend beyond weighted vectors and sentence-level embeddings. Spacy can provide detailed word embeddings at the word level that capture fine-grained semantic information. These embeddings are particularly useful for tasks like word sense disambiguation and detecting synonyms, which are vital for journalists striving to improve the clarity and variety of their writing. Moreover, Spacy can generate paragraph-level embeddings by aggregating sentence-level embeddings within a paragraph. This offers a holistic representation of the entire paragraph’s content, aiding journalists in understanding the central theme and context of lengthy texts.

Gensim (Doc2Vec): Gensim’s Doc2Vec is another powerful technique for generating document-level embeddings. Unlike word-level embeddings, Doc2Vec generates fixed-length vectors representing entire documents, such as articles, reports, or blog posts. By utilizing this approach, journalists can efficiently compare and find similarities between different pieces of content. Doc2Vec can assist in identifying related articles, allowing journalists to cross-reference information and validate their claims. Moreover, it can be instrumental in organizing vast archives of journalistic content, making it easier to access and retrieve relevant information.

Universal Sentence Encoder (USE): The Universal Sentence Encoder (USE) is a versatile pre-trained model developed by Google. It excels at encoding sentences into fixed-length vectors, regardless of their length or complexity. USE’s strength lies in its ability to understand the semantic meaning and context of sentences, making it a valuable tool for journalists looking to gain insights from large sets of text data. Journalists can use USE to compare the content of different articles quickly. Furthermore, they can employ it for sentiment analysis, identifying emotions associated with specific news stories, and tailoring their approach to engage readers more effectively.