[vtempest]

Here's my approach when I built my text-summary app with TensorFlow's SyntaxNet. SyntaxNet (Parsey) gives part-of-speech for each word and a parse tree showing which less-important words/phrases describe higher-level words. "She sells sea shells, down by the sea shore" => (down by the sea shore) is tagged by SyntaxNet as lower describing "sells" so it can be removed from the sentence. Removing adjectives and prepositional phrases gives us simpler sentences easily. Next, we find key words (central to sentences) for news article based on n-grams, and then score key sentences in which they appear. Use MIT ConceptNet for common-sense linking of nouns and most likely relations between them and similar words based on vectors. Generate article summary from the grammatically simple sentences.

My question is how well the trained models interpret human meaning in joined sentences. I discovered that by simplifying sentences you lose the original meaning when that grammatically-low-importance word is central to the meaning. "Clinton may be the historically first nominee, who is a woman, from the Dem or GOP party to win presidency" is way different meaning than that if you remove the "who is a woman". I am also interested in how it makes sence to join-up nouns/entities across sentences. This will cause the wrong meaning unless you are building the human meaning structures like in ConceptNet by learning from the article itself, as opposed to pretrained models based on grammar or word vector in Gigaword.

My work for the future, is using tf–idf style approach for deciding the key words in a sentence, which I would recommend over relative grammar/vectors. In the example in your blog post ("australian wine exports hit record high in september") you left out that it's 52.1 million liters; but if the article went on to mention or relate importance to that number, by comparing it to past records or giving it the price and so on, you can see this "52.1 million liters" phrase in this one sentence has a higher score relative to the collection of all sentences. As opposed to probabilistic word cherry picking based on prior data, this approach will enable you to extract named entities and phrases and build sentences from phrases in any sentence that grammatically refer to it.

[nl]

a parse tree showing which less-important words/phrases describe higher-level words

Things lower on a parse tree aren't less important than things higher. It just represents a dependency relationship.

[vtempest]

You're pointing out what's already obvious. You still need some way to find what's "less important", which is what the topic is all about, like by using grammar dependency or keywords infrequency.

[nl]

I'm not trying to be hostile, I just don't understand what you mean.

You still need some way to find what's "less important", which is what the topic is all about, like by using grammar dependency or keywords infrequency.

I have some experience in this area[1]. I found keyword frequency worked quite well.

[1] https://news.ycombinator.com/item?id=12356133

[thewhitetulip]

Can I see the code of what you wrote? I am interested in learning tensorflow and syntaxnet.