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Multi-Level Summarization in Instapaper

๐ŸŒˆ Abstract

The article discusses how Instapaper has developed a two-step process to generate article summaries - first extracting the most relevant sentences, and then using the language model ChatGPT to generate an abstract summary. The author covers the technical details of this approach, including performance considerations and internationalization support.

๐Ÿ™‹ Q&A

[01] Extractive Summarization

1. What is the key technique used for extractive summarization in Instapaper? Instapaper uses the TextRank algorithm for extractive summarization, which identifies the most relevant sentences based on their similarity to other sentences in the document.

2. How does the TextRank algorithm work? The TextRank algorithm constructs a graph where each node represents a sentence, and sentences are connected based on overlapping words. It then ranks the sentences based on their importance within the graph.

3. What are the performance characteristics of the extractive summarization approach? The performance of the extractive summarization is directly correlated with the length of the input text. For most articles, the extractive summarization completes within a second, but for longer articles it can take up to 7 seconds. To mitigate performance issues, Instapaper runs the summarization on separate compute-optimized machines and caches the results.

[02] Abstractive Summarization

1. What models did Instapaper evaluate for abstractive summarization? Instapaper evaluated several models including Pegasus-XSUM, T5, ChatGPT-3.5 Turbo, and ChatGPT-4. They ultimately chose to use ChatGPT-3.5 Turbo due to its strong performance and lower cost compared to ChatGPT-4.

2. What challenges did Instapaper face with the abstractive summarization model? One key challenge was that the abstractive summarization model had a maximum context length, and longer articles would exceed this limit. Instapaper explored two solutions - "Sub-Summaries" (summarizing the text in chunks) and "Refine" (iteratively summarizing the text).

3. How did Instapaper's multi-level summarization approach perform compared to the other solutions? Instapaper's multi-level summarization approach, which provides the extractive summary sentences directly to ChatGPT, was found to be faster, cheaper, and produce higher quality summaries compared to the other chunking-based approaches, especially for longer articles.

[03] Internationalization

1. How did Instapaper handle internationalization for the abstractive summaries? Instapaper integrated language detection to determine the language of the input text, and then prompted ChatGPT to respond in the same language. This helped ensure the abstractive summaries were generated in the appropriate language.

2. What challenges did Instapaper face with internationalization? Instapaper found that while the language detection worked well most of the time, ChatGPT would occasionally revert to English even when prompted to respond in the detected language.

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